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少林古传大力鹰爪功-少林鹰爪功

少林古传大力鹰爪功-少林鹰爪功

少林古传大力鹰爪功|少林鹰爪功一、提坛功先用一小口之坛,约重数斤者,此须视练者之原有指劲而定,不行固执,如练者鹰爪之力本可以提十斤之物者,则用十斤以上之坛;如练者鹰爪之力本不足提十斤之物者,则用十斤以下之坛。

此指入手初时而言,所谓约重数斤者,即以此也。

以鹰爪扣于坛口之颈,向上提起。

初时必觉甚为费劲,提起片刻,即须放下,不能耐久。

练习日久之后,此种困难,必渐渐削减,终至于完全不觉,以指提坛,宛如拾芥。

即历时甚久,亦不觉其苦,至此则于坛中加铁屑二三斤,再依法练之,历若干时之后,时间亦无肯定,也许练至不觉其苦,而能长久不坠为止,此则在练时之勤惰而定矣,再加二三斤,如此渐渐增加,苦练不已,由数斤而数十斤;而数十斤,至能以鹰爪之力而提取五十斤之坛,轻如拈羽,历久而不觉其苦,则功造大成矣,以指着人身,必可使人折服,而无能与抗矣。

唯此功夫,须由渐而入,不行越躐。

每次加铁屑,至多不得过三斤,若以贪功之故,而多加重量,非但不能猛进,反足以削减其进步,而兴欲速则不达之叹。

故练功不在于重量增加之速,而在于平常练习时间之长。

大约每日分晨夕二度练习,每一度以练习一个时辰为限。

如此勤行不怠,进步自能神速,若急躁而求功,其结果适得其反,决无良好之成果。

此项功夫,自始至终,大约三年之内,可以成就,其法既简便易行,胜利亦不至过分迟缓。

故练习指功者,多从事于此,良非无故也。

二、石荸荠功石荸荠功夫之练法,与提坛相仿佛,唯以练功之具略异。

而至入手之初,较为困难耳,因坛口有边缘可以借劲,石荸荠则并无边缘可以借劲也。

以青石制成倒置之圆锥形,略如水果中之荸荠状,其重量亦无肯定,大约在入手之初,用二十斤者为度,不行再重矣。

此法与前法稍异者,并不渐次递加。

此法入手即练较原有指力一倍以上之物,以后加重,亦以一倍为宜。

宽阔之处在下,而锐端则向上,练者以鹰爪紧紧扣住其锐端相近之处,用力上提。

在入手之初,以数斤之指力,而提此十数斤之石荸荠,欲其应手离地而起,势所不能,但明知其不能,而必依法勤加练习,以冀其能有提起之日耳。

健身气功(易筋经)ppt课件

健身气功(易筋经)ppt课件
3232总之六字诀是根据祖国医学天人合一总之六字诀是根据祖国医学天人合一生克制化的理论按春夏秋冬四时生克制化的理论按春夏秋冬四时节序配合五脏肝心脾肺肾节序配合五脏肝心脾肺肾属性与角徵宫商羽五音的发音属性与角徵宫商羽五音的发音口型配似呼吸意念和肢体导引引地口型配似呼吸意念和肢体导引引地阴上升吸天阳下降吐出脏腑之浊气阴上升吸天阳下降吐出脏腑之浊气吸入天地之清气结合后天之营卫推动吸入天地之清气结合后天之营卫推动真元使气血畅行于五脏六腑之中以达真元使气血畅行于五脏六腑之中以达缺身心康健益寿延年之实效
23
歌云:

春嘘明目夏呵心,秋呬冬吹肺肾宁。 四季常呼脾化食,三焦嘻出热难停。 发宜常梳气宜敛,卤宜数叩津宜咽。 子欲不死修昆仑,双手摩擦常在面。
24
嘘字诀 春嘘明目 以嘘治吁气

25
呵字诀:夏呵心,以呵治心气

26
呬字诀 秋呬宁肺,以呬治肺气

27
呼字诀四季常呼脾化食,以呼治脾气

28
吹字诀 冬吹肾宁,以吹治肾气
第九势:青龙探爪势
• 两脚开立,两手成仰拳
护腰。右手向左前方伸探, 五指捏成勾手,上体左转。 腰部自左至右转动,右手 亦随之自左至右水平划圈, 手划至前上方时,上体前 倾,同时呼气:划至身体 左侧时,上体伸直,同时 吸气。左右交换,动作相 反。连续5-10 次。
诀曰:青龙探爪,左 从右出;修士效之,掌气 平实;力周肩背,围收过 膝;两目平注,息调心谧。
正直,环拱手当胸,
气定神皆敛,心澄
貌亦恭。
6
第二势:韦驮献杵第二势
• 两手下按,提掌前推,
两手平开,翻掌提踵 两足分开,与肩同宽, 足掌踏实,两膝微松; 两手自胸前徐徐外展, 至两侧平举;立掌, 掌心向外;两目前视; 吸气时胸部扩张,臂 向后挺;呼气时,指 尖上翘,掌向外撑。 反复进行8-20 次。

学习辅助网www5730net莘...

学习辅助网www5730net莘...

作者楊傾雲qq1595794329气行身动似树长花开,神恬意静如母怀婴睡。

内家三拳,譬如一树。

形意干也根也,八卦枝也节也,太极花也叶也。

形意以崩拳最为有名也,崩拳属木,而木出于土,土乃先天一横也。

崩出于横犹如干出于根,形意善纵,然纵之本又是横也。

八卦以变化为妙,所谓横生枝节是也。

节生枝枝生节生生不息变化无穷也。

为何总绕树而转?以纵生横也。

太极者,一理也话,是说给能听动的人听的;书,是写给能读懂的人读的;拳,是传给能练成的人练的。

能听懂,必是耐心聆听之人;能读懂,必是用心拜读之人;能练成,必是潜心研练之人。

所谓功夫不负有心人,有心方能用功,用功方能有成也。

终于明白裘德元先生为什么要身披五十斤重的铜衣练三体式了……原来人体的清阳之气远胜于浊阴之气时,地球对人之引力已极小,此时站三体式双胯根本感觉不到上身的重量,胯根再难以得到锻炼,内抽不到极处矣!加上一定重量后,上身自然下沉的重力一下子落在了胯根,横向内抽之劲与垂直下落之力相合互消…当年在南粤羊城,骄阳似火的盛夏,我门窗紧闭,在烤箱一样的房间里,醉舞八卦,挥洒太极,静练形意,从汗如雨下,臭气熏鼻,到微汗不出,香透全身,我付出的是寂寞,经历的是极乐,身在俗世,心已解脱,不愿再过凡人生活;如今处北国保定,冰天雪地的隆冬,我穿薄盖单,且练且眠,不觉有甚微寒地球,公转平均速度为29.79千米/秒,在远日点公转的速度为29.3千米/秒;在近日点公转的速度为30.8千米/秒。

我们的家园昼夜奔驰,千古不懈,万物寄居其中而不觉其动,何也?盖因天地万物一体也,一动同动,动静合一也。

八卦拳之妙,亦如是也。

不要说你没看见就是假的,不要认为你做不到别人也不行。

宇宙太虚何其大也,你能看见几颗星?有些星星几千年前就不在那里了,可是你现在才看到;有些星星现在就在那里,可是你得等到几千年后才能看到。

所以,我们怎么能以自己有限的眼光去衡量这个无限的世界呢?国术人对厚黑学的运用原则:艺成前:1.脸皮只对师父一人厚,不把师父的本领通通学到手,师父怎么赶都不走!2.心子只对自己黑,披荆斩棘不后退,一个动作练不对,千遍万遍不叫累!艺成后:1.宽厚敦厚,待人接物温和有礼,容人但不受辱!2.心黑手黑,锄凶制暴铁面无私,动手绝不留情!天下本无路,大地任我走。

健身气功

健身气功

健康猫APP-让运动不寂寞/健身气功导师林剑峰崇尚武德修身养性健康自信身心和谐气功发源于中国。

气功在中国有悠久的历史,有关气功的内容在古代通常被称为吐呐、行气、布气、服气、导引、炼丹、修道、坐禅等等。

中国古典的气功理论是建立在中医的养身健身理论上的,自上古时代即在流传。

气功是一种以呼吸的调整、身体活动的调整和意识的调整(调息,调形,调心)为手段,以强身健体、防病治病、健身延年、开发潜能为目的的身心锻炼方法。

健康猫APP-让运动不寂寞/气功有助于体势的放松,也有助于精神的宁静,让人心平气和地面对生活,保持阳光、快乐的心态。

气功能够强身健体,通常与武术相结合。

其实,气功与中医也是相辅相成的,通过主动的自我精神活动来调整自身的生理活动,在气功入静状态下调动和培育人体的生理潜力,可以起到强身怯病的作用。

修身养性,陶冶情操,修练气功,让人心境平和,在强身健体的同时,由内而外地促进生理调节。

气功通过意识增强人的自我调节能力,身心并重,达到形神统一、身心和谐的境界。

今天我们邀请到的是健身气功导师林剑峰,他在气功修炼的过程中获取健康与快乐。

他热爱气功,也热爱户外活动,性格开朗的他在工作之余也喜欢游历美景,体验大自然。

他认为气功不仅有利于身体健康,而且对心境也有调节作用,令他受益匪浅。

就让他给我们分享一下他练气功的心得与体会吧!健康猫APP-让运动不寂寞/林剑峰,自幼习武,崇尚武德,有侠义之心,从事专业训练十余载,毕业于华中师范大学体育学院,硕士研究生,中国武术五段,健身气功社会指导员,健康猫运动生活馆7号馆健身气功导师,专业教学五禽戏、六字诀等康复养生功法,疏通经络,调和气血!主要奖项:2012年泉州市健身气功站点联赛一等奖2014年福建省健身气功联赛第一名2015年海峡两岸健身气功交流赛二等奖健康猫APP-让运动不寂寞/健康猫:林老师,气功是一种中国传统的保健、养生、祛病的方法,在这里,你可以讲解一下气功对人的身心有哪些调节作用吗?林剑峰:健身气功是双向调节的锻炼过程。

武术功夫爱好者必看 各种拳术视频地址

武术功夫爱好者必看 各种拳术视频地址

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94 [拳友俱乐部]*猿功拳/f?kz=561886366 95 [拳友俱乐部]*唐拳/f?kz=561884372 96 [拳友俱乐部]*以色列近战术/f?kz=582790932 97 [拳友俱乐部]*马来西亚武术-希拉/f?kz=582809197 98 [拳友俱乐部]*柬埔寨血斗/f?kz=582821257 99 [拳友俱乐部]*俄罗斯桑搏/f?kz=582823072 100 [拳友俱乐部]*海军格斗术/f?kz=582827308101 [拳友俱乐部]*古希腊式搏击/f?kz=582831234 102 [拳友俱乐部]*MMA极限格斗/f?kz=582836911 103 [拳友俱乐部]*菲律宾武术及短棍/f?kz=582845313 104 [拳友俱乐部]*二路炮拳/f?kz=509349462 105 [拳友俱乐部]*四明内家拳/f?kz=509633433 106 [拳友俱乐部]*武当太乙五行拳/f?kz=509642465 107 [拳友俱乐部]*少林大洪拳/f?kz=508797295 108 [拳友俱乐部]*少林童子功/f?kz=509647165 109 [拳友俱乐部]*少林绝学--铁砂掌/f?kz=509656200 110 [拳友俱乐部]*武术套路类/f?kz=506900654 111 气功/f?kz=509313747 112 [拳友俱乐部]*武当武术——玄武拳/f?kz=509673176 113 [拳友俱乐部]*武当伏虎拳/f?kz=509311403 114 [拳友俱乐部]*蛇拳/f?kz=510775539 115 [拳友俱乐部]*猴拳/f?kz=510787300 116 [拳友俱乐部]*少林龙拳/f?kz=508584169 117 [拳友俱乐部]*通臂拳/f?kz=510796892 118 [拳友俱乐部]*巴西踢/f?kz=511043819 119 [拳友俱乐部]*少林拳绝学-铁布衫,铁裆功/f?kz=509325051 120 [拳友俱乐部]*虎鹤双形拳/f?kz=511057866 121 [拳友俱乐部]*武当拳/f?kz=509637284 122 [拳友俱乐部]*少林心意把/f?kz=511089454/f?kz=511097330 124 [拳友俱乐部]*迷踪拳/f?kz=511099247 125 [拳友俱乐部]*七星螳螂拳/f?kz=509676611 126 [拳友俱乐部]*鹤拳/f?kz=511113936 127 [拳友俱乐部]*少林连手短打/f?kz=511127471 128 [拳友俱乐部]*五步拳/f?kz=511136275 129 [拳友俱乐部]*鹰抓拳/f?kz=508563262 130 [拳友俱乐部]*少林柔功/f?kz=509658173 131 [拳友俱乐部]*洪拳/f?kz=511613802 132 [拳友俱乐部]*五祖拳/f?kz=511622036 133 [拳友俱乐部]*太乙逍遥掌/f?kz=508573594 134 [拳友俱乐部]*五步拳/f?kz=508794558 135 [拳友俱乐部]*八卦剑/f?kz=509226367 136 [拳友俱乐部]*鹤拳道/f?kz=516265921 137 [拳友俱乐部]*少林棍/f?kz=512145346 138 [拳友俱乐部]*缩骨功/f?kz=512145761 139 [拳友俱乐部]*铁头功/f?kz=512146533 140 [拳友俱乐部]*蛤蟆功/f?kz=512147476 141 [拳友俱乐部]*弹指神通/f?kz=512148634 142 [拳友俱乐部]*降龙十八掌/f?kz=512149848 143 [拳友俱乐部]*木兰拳/f?kz=512265281 144 [拳友俱乐部]*蔡李佛拳/f?kz=511616716/f?kz=512149088 146 [拳友俱乐部]*长拳/f?kz=508801074 147 [拳友俱乐部]*少林易筋经/f?kz=511054780 148 [拳友俱乐部]*八卦掌/f?kz=509217464 149 [拳友俱乐部]*文圣拳/f?kz=511996002 150 [拳友俱乐部]*虎鹤双形拳/f?kz=511963957 151 [拳友俱乐部]*枪术/f?kz=508793323 152 [拳友俱乐部]*翻子拳/f?kz=511937058 153 [拳友俱乐部]*戳脚/f?kz=511953986 154 [拳友俱乐部]*南宫靠拳/f?kz=514387602 155 [拳友俱乐部]*花拳/f?kz=511961466 156 [拳友俱乐部]*形意拳/f?kz=511961466 157 [拳友俱乐部]*刀术/f?kz=508792946 158 [拳友俱乐部]* 忍术/f?kz=548107986 159 [拳友俱乐部]*象形拳/f?kz=534772365 160 [拳友俱乐部]*无限制格斗/f?kz=538323464 161 [拳友俱乐部]* 自然门/f?kz=538858867 162 [拳友俱乐部]* 健身/f?kz=548107986 163 [拳友俱乐部]*法国踢击术/f?kz=523553760 164 [拳友俱乐部]*擒拿格斗拳友/f?kz=507446479 165 [拳友俱乐部]*醉拳/f?kz=508559643 166 [拳友俱乐部]*摔跤/f?kz=508559643167 [拳友俱乐部]*摔角/f?kz=523384666 168 [拳友俱乐部]*踢拳道/f?kz=521786902 169 [拳友俱乐部]*女子自卫格斗/f?kz=521786902 170 [拳友俱乐部]*南拳/f?kz=508776183 171 [拳友俱乐部]*九节鞭/f?kz=511624477 172 [拳友俱乐部]*棍术/f?kz=508785220 173 [拳友俱乐部]*巴西柔术/f?kz=508519852 174 [拳友俱乐部]*舞狮绝技/f?kz=508519852 175 [拳友俱乐部]*十八般兵器/f?kz=509628200 176 [拳友俱乐部]*轻功/f?kz=509303162 177 [拳友俱乐部]*剑术/f?kz=508781208 178 [拳友俱乐部]*跑酷/f?kz=508781208 182 [拳友俱乐部]*硬气功/f?kz=508520525 183 [拳友俱乐部]*点穴功夫/f?kz=509316195 187[拳友俱乐部]*医术/f?kz=715725573。

气功与健身(上下两册全)

气功与健身(上下两册全)
调病要取远距离的穴,调理头部最好别直接在头上动,更不能用排山掌。得用咱们这个手型,用手指点按,上病下治,远距离发功,别“嘣”一下子把人打得坐在那里,还不如踢他两脚让他走,很可能反而好。他意念在头,踢脚踢痛了,意念一下来,头马上就好了。
人类有文字记载的历史只有几千年。大自然运动的结果出现了人类,人是万物之灵,是地球的主宰。炼丹时看得神乎其神,这时不能动摇,否则出偏。不是青蛙、老鼠、蛇……,不是它们主宰地球,不动摇就赢了。人是动物进化的高级成果,电视等等高科技都是人脑的产物,物质第一性的观点永不动摇。练功路上悠然自得,万般变化皆为假。让我们共同荡起双桨顺利到达彼岸,不要一个浪头打来就被打翻了。
我初到长春的时候,大家出难题,认认真真考我。一个医院的院长,副教授,肺有个洞。几个医生说用五分的硬币才能盖住,我说只有一分硬币大,是膨胀型的,旁边是虚影,我把虚影拿掉只一分钱大小。我说宁愿说小点,别增加负担。他们宁愿说重一点,把握大一点,但是病人压力大。后来调好了,洞没了,当上了正教授。
调脑血管病得慢慢从细头往粗头走。轻轻地调,光本身会转,在局部转,一疏通,就开了,功神型的,感觉不强烈,但起作用。
我讲话是带功的,现在收功,搓搓手,哪不舒服,拍打拍打。这个班大家提问题,我来回答。出来传功十多年了,还没有把我考住的,希望你们出一二十个问题把我考住。
(四)
气功调病是双向调节。什么叫双向调节?就是把经络疏通一下,再发光清理一下病灶。气功师与病人相互配合,这就是双向调节。气功治病不像大夫给点药,如镇痛片或消炎药等,看得见,气功调病,你没功能就看不见。不舒服再做,再不舒服,反复做,经过三五次,10次、20次,病就正过来了,一定要坚定信心。
中乘功法是传教。道家功说我是道家的,不许你佛家来掺和,传佛家功的要道家走开,治不好病就让你去买个佛像来,人家买了,病不好又扔掉,令人看了好笑。中乘的特点是传教。

武术网站大全

武术网站大全

武术初学者视频小洪拳十路弹腿ftp://vcd:21wulincom@218.28.14.36/武术初学者教学录像/少林小洪拳上.rmvb ftp://vcd:21wulincom@218.28甘奉池的少林花拳mms:///vod/v1/huaquan/01.asf少林资料都是年轻人练习的。

学习好的东西还是跟老架拳谱的人物学。

这里的提供个人感觉价值不是很高。

但健身是足够了。

少林童子功mms:///vod/v1/shaolin/01.asf少林齐眉棍mms:///vod/v1/shaolin/02.asf小洪拳mms:///vod/v1/shaolin/03.asf醉拳mms:///vod/v1/shaolin/04.asf鹰爪拳mms:///vod/v1/shaolin/05.asf套路欣赏mms:///vod/v1/shaolin/06.asf龙拳mms:///vod/shoufei/gongfuboji/20050510-24.wmv三节棍mms:///vod/shoufei/gongfuboji/20050509-56.wmv双鞭mms:///vod/shoufei/gongfuboji/20050509-57.wmv这个是精品少林北派镇山棍mms:///vod/shoufei/gongfuboji/20050508—12.wmv杨俊敏博士擒拿技术解析/2005/10/19/0000070289.html神虎擒拿术/2005/09/04/0000063775.html格雷西柔术权威教程:初级篇/2005/12/08/0000078634.html通背拳视频大集合钟国麟通背拳/CLIPS/TONGBEI/tong_bei.mpg太极五行通背拳基本功mms:///vod/v1/taijiwuxingtongbiquan/01.asf mms:///vod/v1/taijiwuxing张贵曾白猿通背拳连环肘mms:///vod/shoufei/gongfuboji/20050510-65.wmv白猿通背拳摔法mms:///vod/shoufei/gongfuboji/20050509-28.wmv白猿通背拳实战技法mms:///vod/shoufei/gongfuboji/20050510-65.wmv赵建国通背十二连拳mms:///vod/shoufei/gongfuboji/20050509-29.wmv破门三十二mms:///vod/shoufei/gongfuboji/20050509-31.wmv mms:///vod/sho 珍藏精品视频(心意六合)(八极)心意六合拳珍藏ftp://vcd:21wulincom@218.28.14.36/心意形意拳/[吴秋亭解兴帮系心意拳].rmftp://vcd:21wulincom@218.28.14.36/QQ95238最新上传/李洳波心意六合拳心意把vcd清晰.rmvbftp://vcd:21wulincom@218.28.14.36/QQ95238最574629545 20:29:22八极拳大全吴连支部分ftp://vcd:21wulincom@218.28.14.36/QQ95238最新上传/八极拳基本功与套路欣赏.DATftp://vcd:21wulincom@218.28.14.36/QQ95238最新上传/八极拳(BaChi) - 吴氏- 开门八极小架一路教学.a ftp://vcd:21wulincom@218.28.14.36/QQ95238最新上传/开门八极拳单打与对打.wmvftp://vcd:21wulincom@218.28.14.36/QQ95238最新上传/八极拳四郎宽.DATftp://vcd:21wulincom@218.28.14.36/QQ95238最新上传/八极拳(BaChi) - 吴氏- 八极单打.mpgftp://vcd:21wulincom@218.28.14.36/QQ95238最新上传/八极拳(Baji) - 吴氏- 八极六开拳(bachi6).mp 台湾八极拳+长春八极拳ftp://vcd:21wulincom@218.28.14.36/QQ95238最新上传/刘云樵- 八极连环拳.mpgftp://vcd:21wulincom@218.28.14.36/QQ95238最新上传/长春八极拳_谭吉堂.rmftp://vcd:21wulincom@218.28.14.36/QQ95238最新上传/八极拳- 八极连环.ram∙2006-11-13 05:16∙回复2楼ftp://vcd:21wulincom@218.28.14.36/QQ95238最新上传/[武术]长春八极拳八大招_谭吉堂.rarftp://vcd:21wulincom@218.28.14.36/QQ95238最新上传/[武术]长春八极拳六大开_谭吉堂.rarftp://vcd:21wulincom@218.28.14.36/QQ95238最新上传/[刘云樵][八极拳].mp4ftp://vcd:21wulincom@218.28.14.36/QQ95238最新上传/[VCD][武术][八极门系列]小八极[TWed2k.Fox.TWpower ftp://vcd:21wulincom@218.28.14.36/QQ95238最新上传/[VCD][武术][八极门系列]大八极[TWed2k.Fox.TWpower ftp://vcd:21wulincom@218.28.14.36/QQ95238最新上传/[VCD][武术][八极门系列]八极连环[TWed2k.Fox.TWpow太极----陈式老架一路太极拳陈正雷版》陈正雷演练讲解版/2005/07/02/0000055780.html《陈氏太极拳老架二路》陈正雷版/2005/10/06/0000068157.html《陈氏太极拳推手》陈正雷版/2005/10/06/0000068164.html《陈式太极拳32摔法、32擒拿实战门子》/2005/04/19/0000047184.html《太极等武术资料》(taichi)/2005/04/21/0000047429.html下载也可以在线看资源陈式太极拳老架一二路mms:///vod/v1/chenshitaijiquan/16.asf mms:///vod/v1/chenshitaijiquan/17.陈式太极拳忽雷架mms:///vod/v1/chenshitaijiquan/05.asf mms:///vod/v1/chenshitaijiquan/06. s:///vod/v1/chenshitaijiquan/10.asf mms:///vod/v1/chenshitaijiquan/11.as 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少林内功心法——少林气功

少林内功心法——少林气功

少林内功心法——少林气功少林气功不仅仅只用于硬气功,少林七十二艺可以说是少林气功绝学。

少林功夫的深浅从何表现?功夫界有句俗语:外练筋骨皮,内练精气神(或内练一口气)。

而从精到神的过程必须是炼精化气,炼气还神。

可见气的重要。

当然少林功夫还有更高的层次,禅武如一。

然而禅是意识的,武是肉体的,如何如一?从少林的六合理论解释最为简单,有外三合和内三合。

内三合为:心与意合,意与气合,气与力合。

还是离不开气。

全日本少林气功协会会员向少林寺僧众汇报展演打拳练功常有这句话:气沈丹田。

作为健康法门的佛医,禅医,祖国医学告诉我们:所有疾病的原因皆因阴阳失调,这里的阴阳当然是指阴阳二气。

而所有的治疗原则及健康标志是阴平阳秘,平衡阴阳。

反映到脏腑即为心肾相交。

即代表臓腑阳气之心火之气与代表脏腑阴气之肾水之气交流平衡。

然而心肾相距甚远,只有气的流动才能达到很好的交流。

因此中医的所有治疗方法都是为此目的。

然而气功导引来的更快更直接。

因此少林气功是联接、通向禅武医的捷径。

更是健康法门和现代禅修的简便方法。

为报答有缘之士,现简单介绍几种方法。

一、打坐(即静功)很多想修行的人都喜欢打坐,但苦于没有方法,结果杂念纵生,昏昏欲睡,无法持续下去。

因此,消除杂念是打坐的前提,为此可归纳几种方法:第一、呼吸入静法:即利用呼吸而达到消除杂念入静的方法。

主要有数息,即修数、证数,方法是数呼吸从1到10,条件是不能有杂念。

如果有杂念了再从1开始。

一直重复下去。

如果数到10没有杂念就不要继续数了,因为已达入静状态,已达目的,手段方法就不要了。

还有就是随息、即修随证随。

方法是意识随着呼吸,即观想意守呼吸,这样既使生来就思绪不断的大脑也顾不上了。

还有止息观息还息净息。

限于篇幅就不再介绍了。

姿势:双盘、单盘、散盘都可。

两手结金刚印,即在小腹前两手重叠,大姆指接触呈椭圆形,男士右手在上女士左手在上。

舌扺上腭,腰背放松伸直,面带微笑,两个眼微闭。

第二、观想(意守)入静法。

太极拳书籍下载汇总

太极拳书籍下载汇总
[url=/1326055.html]下载:彭元植秘传龙虎太极拳_林国华.PDF[/url]
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[url=/1326324.html]下载:世傳陳式太極拳_陈小旺.pdf[/url]
[url=/1331262.html]下载:杨式太极拳用法解要_庞大明.pdf[/url]
[url=/1331334.html]下载:杨禹廷太极拳系列秘要集锦_李秉慈.pdf[/url]
[url=/1331342.html]下载:中国陈氏太极_陈正雷.pdf[/url]
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[url=/1323488.html]下载:《太极拳浅说》-徐致一.pdf[/url]
[url=/1323933.html]下载:《太极拳使用法》-董虎岭.pdf[/url]
[url=/1324785.html]下载:陈式太极拳拳法拳理_陈照奎传 马虹编著.pdf[/url]
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武术资源汇总帖

武术资源汇总帖
ed2k://|file|[以弱胜强百法].丛亚贤.扫描版.pdf|3030347|e2dbda42c0de22b47466b2bb58ba9539|h=y7xmxb5dulfskg4itava4fdm6klyg2ta|/
ed2k://|file|[国际自由搏击精粹].朱建华.扫描版.pdf|5047111|6e2969ec226b7180dd3ab73adfe95418|h=pvgo3yf7axwonceequ4jdwy7vlcjlhab|/
ed2k://|file|[截拳道搏击精要].高谊.扫描版.pdf|7047311|73ed63c10f1a350b5d4edd5642a18a19|h=owdzigcici26xvs43cpethdqjbqjdgss|/
ed2k://|file|[先发制人百法].王力.扫描版.pdf|2442472|f5801e17e4d4dffbc6b5cf558445823f|h=sepd7nl7uuzhc7irdtrsomjrqll3vz4o|/
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ed2k://|file|[警用匕首格杀十八法].杨建南.扫描版.pdf|2490937|7271b41f9edd6f23ffe1a7c2b23f2be2|h=y5cfntid2eig6qthobchraqe7544vbs4|/
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必看—下载网址

必看—下载网址

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古武网——精选推荐

古武网——精选推荐

古武⽹武术介绍武术起源 从奴⾪社会开始,古代统治阶级为了维护其统治,就必须垄断⼀切军事⼿段。

军事训练的主要形式是“⽥猎”和“武舞”。

⽥猎的⽬的,是训练对各种武器的使⽤及驭马驾车技术,是纳⾝体、技术、战术的训练为⼀体的综合训练。

据《礼记·⽉令》载:“天⼦易教于⽥猎,以习五戎”,五戎即⼸⽮、⽎、⽭、⼽、戟五种兵器。

⽥猎活动也包括“空⼿以搏”和“⼿格猛兽”的搏⽃技能。

利⽤⽥猎进⾏军事训练,不仅商代如此,直到西周春秋也是如此。

只是商代⽐较简单,到西周时才逐渐制度化。

 武舞也是训练的⼀种形式。

《尚书·牧誓》载:“夫⼦助哉!不愆于四伐、五伐、六伐、七伐乃⽌,齐焉。

”(《尚书正义》卷⼗⼀)伐是击刺之意。

⼽是击兵,⽭为刺兵,⼀击⼀刺为⼀伐。

这是根据两种兵器性能相结合,把击刺同阵形、队形结合起来的武舞式的训练。

这种将⽤之于实战的格杀经验,按⼀定程序来训练,是古代武术由感性认识向理性认识的升华,是由⽀离破碎向系统化演进的象征,也是早期武术器械已具雏形的象征。

阅读《武术起源》查看全⽂。

武术发展 春秋初期的民间已有了习武之风。

管仲在齐国要求⼠⼤夫举荐“有拳勇股肱之⼒,筋⾻秀出于众者”,说明齐国不乏“有拳勇”拔萃的⼈物。

“齐⼈隆技击”(《荀⼦·议兵》),⼜说明齐国民间习武盛⾏。

同期已能出产质精物美的青铜剑,出现了⼲将、欧冶⼦等最负盛名的造剑匠。

吴越地区也是击剑盛⾏之地,出现过精于“⼿战之道”的越⼥。

北⽅的赵国赵⽂王养“剑⼠夹门⽽客三千余⼈。

⽇夜相击于前”(《庄⼦·说剑篇》)。

同时,社会上还出现了职业武⼠,如侠⼠之类,典籍上称为“侠”“节侠⼠”“游侠”。

社会上以练武为职业⼈的出现,对武术技艺的提⾼和推动武术技术的发展有着重要作⽤。

 随着民间武艺⽇盛,武术开始往庞杂化⽅向发展了。

以个⼈技艺为主的徒⼿搏技⼀⼀⼿搏、⾓⼒,在民间有⼴泛的市场。

春秋时称为相搏,相搏可⽤拳打脚踢,连摔带拿,运⽤奇巧战术来制胜对⽅。

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可在线传送的电子书目录 各类武术书

可在线传送的电子书目录 各类武术书

分类心意心意拳马琳璋pdf格式文件大小:11.10MB)心意六合拳艺传真pdf格式文件大小:4.10MB)清晰度不很好心意六合拳陶子鸿pdf格式文件大小:2.75MB姬龙峰祖师原传心意六合古拳法成元昭pdf格式文件大小:7.49MB范氏心意拳谱pdf格式文件大小:(3.47MB)心意拳练功窍要马琳璋pdf格式文件大小:29.50MB陈守孚老师心意拳初修法要辑录pdf格式文件大小:886.00KB内家拳功与心意六合拳顾凤宾pdf格式文件大小:(17.70MB)心意六合拳真传实战教程训练目标实施手册(马胜利).pdf”(38.30MB)戴氏心意拳郭瑾刚pdf格式文件大小:(51.40MB)戴氏心意拳霍永利pdf格式文件大小:(5.09MB) 本书全部为照片心意六合拳李洳波pdf格式文件大小:(38.50MB)华岳心意:三盤十二式-心意六合八法拳陈亦人pdf格式文件大小:(2.81MB)心意六合拳发力与技击王安宝魏峰pdf格式文件大小:(4.62MB)山西六合心意拳张友林pdf格式文件大小:(1.17MB)纯文字无图心意六合拳十大真形单操与技击闫无为pdf格式文件大小:(13.30MB)心意门秘籍李新民pdf格式文件大小:(5.17MB)洛阳心意拳孙友恒(不清晰)pdf格式文件大小:(42.30MB)心意六合拳徐谷鸣pdf格式文件大小:(46.00MB)少林心意拳贾召宣pdf格式文件大小:(6.84MB)心意太极拳马琳璋马天巧pdf格式文件大小:(6.41MB)心意六合拳秘传(陶景和).pdf”(6.27MB)形意孙禄堂武学录孙禄堂著孙剑云编pdf格式文件大小:(6.79MB)拳意述真孙禄堂中华书店影印版pdf格式文件大小:21.10MB形意拳真传图谱李金波pdf格式文件大小:(7.30MB)形意拳术体用全书技法篇孙绪pdf格式文件大小:(88.20MB)形意拳术抉微刘殿琛pdf格式文件大小:(7.76MB)形意拳术大全(第二版) 吴殿科pdf格式文件大小:(252.00MB)形意拳入门讲座李文彬(杂志文章精选)pdf格式文件大小:(8.80MB)古拳谱形意拳秘法高降衡(杂志文章精选)pdf格式文件大小:(8.28MB)形意拳练法用法与功法曹志清pdf格式文件大小:(5.66MB)形意母拳姜容樵pdf格式文件大小:( 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形意拳术体用全书五行功法篇孙绪编著pdf格式文件大小:(59.10MB)形意拳术李天骥pdf格式文件大小:(5.62MB)带网址广告,不清楚形意拳孙汝贤pdf格式文件大小:(4.35MB)中国尚氏形意拳(苏立志)pdf格式文件大小:(34.20MB)形意拳实战技法张桐pdf格式文件大小:(1.79MB)形意拳杂式捶姜容樵pdf格式文件大小:(11.40MB)形意拳术李剑秋pdf格式文件大小:(1.12MB)形意拳谱刘纬祥pdf格式文件大小:(1.62MB)形意大连环沙国政审定pdf格式文件大小:(40.70MB)宋氏形意拳续编上篇宋光华pdf格式文件大小:(9.08MB)形意八卦掌-徐连生杂志连载共22页pdf格式文件大小:(1.38MB)形意拳:中国武术系列规定套路pdf格式文件大小:(3.57MB)形意拳器械技击功法(第三集) 房丹才pdf格式文件大小:(23.00MB)形意拳器械技击功法(第二集) 房丹才pdf格式文件大小:(26.20MB)形意拳器械技击功法(第一集) 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李小龙技击术——擒拿技击术(黄明贵).pdf.pdf”(4.22MB)李小龙功夫教程.pdf.pdf”(12.30MB)李小龙传世绝技截拳道腿法功夫(魏峰).pdf.pdf”(2.80MB)李小龙-魏峰着.pdf”(20.40MB)截拳道连环技法(舒建臣).pdf”(7.37MB)截拳道攻防技法(舒建臣).pdf”(9.00MB)抗暴绝技李小龙制暴自卫术(李涛).pdf.pdf”(3.30MB)截拳道—李小龙原著(38.20MB)菲律宾魔杖:李小龙短棍鼻祖(闫无为).pdf.pdf”(11.20MB)振藩截拳道:李小龙生平、拳理和格斗技艺魏峰(6.27MB)一代武星李小龙.pdf.pdf”(2.32MB)图解截拳道技击全书(戴相国).pdf”(4.84MB)全图解李小龙腿法(温戈).pdf”(10.40MB)神功绝技:李小龙二节棍攻击法].董子红.扫描版.pdf”(2.00MB)截拳道两节棍技法汇宗(李向伟).pdf.pdf”(6.71MB)全图解李小龙二节棍(温弋).pdf.pdf”(20.80MB)二节棍(吴志勇).pdf.pdf”(5.67MB)一代武星李小龙(沉理然).pdf”(3.05MB)海外武技:泰国拳:精解泰拳绝命技.pdf”(3.96MB)帕亚泰拳.pdf”(37.30MB)[图解泰拳实战技法].曹建泉.周共培.扫描版.pdf”(11.60MB)[泰拳技巧图解].武兵.扫描版.pdf”(6.19MB)泰拳腿法秘技-前胫猛踢马胜利(清晰度不佳).pdf”(9.64MB)泰国拳:最具实战威力的泰拳技法.pdf”(1.42MB)泰拳基础训练读本(舒建臣).pdf”(20.90MB)[巴西柔术降服技抓把技术扫描版.pdf”(37.80MB)[巴西柔术].魏峰.&.席连正.扫描版.pdf”(14.60MB)[柔道的戒律].(Canon.of.Judo).三船久藏.扫描版.pdf”(20.20MB)图解柔道:摔的技巧(工藤一三).pdf”(28.10MB)柔道入门(李其彪).pdf”(2.19MB)柔道教程(日本)醍醐敏郎.pdf”(23.70MB)柔道:以柔克刚的日本国技(李贤英).pdf”(3.08MB)五轮书(日本)宫本武藏pdf”(6.75MB)综合格斗.技术手册].(Mixed.Martial.Arts.The.Book.of.Knowledge).BJ.Penn.pdf”(47.00MB) 短棍术Inosanto,.Dan.-.The.Filipino.Martial.Arts.pdf”(11.10MB)“Hine,John.Y ang.Tai.Chi.Chuan.1992.OCR.6.0.pdf”(43.20MB)《美军生存手册》(美国陆军司令部)英文- 文字版[PDF].pdf”(7.03MB)前苏联克格勃夺命利器—桑搏擒摔术.pdf”(2.79MB)42天空手道训练速成教室.pdf.pdf”(4.43MB)图解空手道技击全书(彦红).pdf”(4.03MB)空手道摔技(尹伟).pdf”(1.10MB)空手道(刘同为).pdf”(3.66MB)日本空手道入门[日]藤本贞治.pdf”(3.06MB)全接触空手道(朱建华).pdf”(11.50MB)跆拳道八段名师话踢法(刘少海).pdf”(20.60MB)跆拳道(刘宏伟).pdf”(2.02MB)中国跆拳道实战100例(岳维传).pdf”(15.30MB现代跆拳道运动教学与训练(王智慧).pdf”(29.70MB)搏击:明星技术(英)戴维·米切尔.pdf”(7.07MB)[藏传密宗气功那若六成就法].玛尔巴译师.扫描版.pdf”(8.59MB)军警格斗2001年版美陆军格斗教材U.S.Marine.Corps 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气功

气功

卷三《硬功禅秘》
本门之大力阴风掌,卷一则以修练内功,卷二则是锻炼外功,实际是以气化力,气力合一的内壮神功,本卷则是外壮之功,如果只修得前两卷内壮之功而不修本卷之功,则全功只得十之五、六,故而欲习得上乘绝学,必须内功,外功,硬功,软功合一方可。
本门大力明风掌之硬功共分三式,现分述于下:
2.在山岭上的崎呕小道,旷野不平的地方练习跑,每天渐渐增加路程,直至一次跑40里,两次跑80里。 3.在陡壁悬崖上,山路崎岖的地方,练习跑路,沙袋、沙衣渐渐增加至60斤,能穿在身上,缚在腿上,运用自如,每天早晚两次,每次50里路,全天跑路100里。连续不间断跑四年半。能在凹凸不平之路上,爬上跳下轻如灵猿,要持恒苦练,但也不可过急,以免损伤身体。须下六年苦功夫、八年恒功,方一次可以疾行100里。
(三)鸳鸯浮水
先以两掌触地,身地下蹲,上体略前倾,双膝跪于两肘上,两脚慢慢离地。成静止势,双目微闭,舌轻抵上腭,收肛提阳,自然呼吸。成静止势后,吸气时意想地气从掌心劳宫穴入内,沿手三阴经至膻中穴,沿任脉进入丹田。呼气时意想丹田之气从丹田沿任脉至膻中穴,从膻中穴分两股沿手三阴经奔注于劳宫穴。由劳宫穴奔泻而出。如此练习,时向逐渐增长,力乏而止、如此能练三十分钟左右,则可改练下势。
然后,脚不动两手成八字掌。掌心朝上从体则上托至头顶,变掌心朝下下按至小腹部。同时意想地气沿双腿随上托之动作上行至头顶百会穴(注:此动意想,不是意想某一线,或一片,而是沿腿之整体),然后又随下按之动作下行至气海穴(注:此动意想内气所走路线为冲脉)。然后双手外旋,掌心由下朝内,左手在内,右手在外重叠放于小腹上。吃气,双手内按,呼气双手放松。同时意想吸气时天地混元之气从四面八方采入丹田中.呼气时丹田内气由丹田向外扩散,如此练习三十分钟左右接练下式。

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Computer aided cytological cancer diagnosis:Cell type classification as a step towards fully automatic cancer diagnostics on cytopathological specimens of serouseffusionsTimna E.Schneider and Andr´e A.Bell and Dietrich Meyer–Ebrecht andAlfred B¨o cking and Til AachInstitute of Imaging and Computer VisionRWTH Aachen University,52056Aachen,Germanytel:+492418027860,fax:+492418022200web:www.lfb.rwth-aachen.dein:SPIE Medical Imaging2007,Computer–Aided Diagnosis.See also B ib T E X entry below.B ib T E X:@inproceedings{SCH07a,author={Timna E.Schneider and Andr\’{e}A.Bell and Dietrich Meyer--Ebrecht andAlfred B\"{o}cking and Til Aach},title={Computer aided cytological cancer diagnosis:Cell type classification as a step towards fully automaticcancer diagnostics on cytopathological specimensof serous effusions},booktitle={SPIE Medical Imaging2007,Computer--Aided Diagnosis},publisher={SPIE},year=2007,pages={in print},}2007Society of Photo-Optical Instrumentation Engineers.This paper was published in SPIE Medical Imaging2007,Computer–Aided Diagnosis and is made available as an electronic reprint with permission of SPIE.One print or electronic copy may be made for personal use only.Systematic or multiple reproduction,distribution to multiple locations via electronic or other means,duplication of any material in this paper for a fee or for commercial purposes,or modification of the content of the paper are prohibited.document created on:February15,2007created from file:cellclassification.texcover page automatically created with CoverPage.sty(available at your favourite CTAN mirror)Computer aided cytological cancer diagnosis: Cell type classification as a step towards fully automatic cancer diagnostics on cytopathological specimensof serous effusionsTimna E.Schneider a,Andr´e A.Bell a,Dietrich Meyer-Ebrecht a,Alfred B¨o cking b and Til Aach aa Institute of Imaging and Computer Vision,RWTH Aachen UniversityTemplergraben55,52056Aachen,GermanyTelephone:+49(241)8027860,Fax:+49(241)8022200Email:{ts,ab,dme,ta}@lfb.rwth-aachen.deb Institute of Cytopathology,Heinrich-Heine-University D¨u sseldorfMoorenstr.5,40225D¨u sseldorf,GermanyTelephone:+49(211)8119346,Fax:+49(211)8118402Email:boecking@uni-duesseldorf.deABSTRACTCompared to histopathological methods cancer can be detected earlier,specimens can be obtained easier and with less discomfort for the patient by cytopathological methods.Their downside is the time needed by an expert tofind and select the cells to be analyzed on a specimen.To increase the use of cytopathological diagnostics, the cytopathologist has to be supported in this task.DNA image cytometry(DNA-ICM)is one important cytopathological method that measures the DNA content of cells based on the absorption of light within Feulgen stained cells.The decision whether or not the patient has cancer is based on the histogram of the DNA values.To support the cytopathologist it is desirable to replace manual screening of the specimens by an automatic selection of relevant cells for DNA-ICM.This includes automated acquisition and segmentation of focused cells,a recognition of cell types,and a selection of cells to be measured.As a step towards automated cell type detection we show the discrimination of cell types in serous effusions on a selection of about3,100manually classified cells.We present a set of112features and the results of feature selection with ranking and afloating-search method combined with different objective functions.The validation of the best feature sets with a k-nearest neighbor and a fuzzy k-nearest neighbor classifier on a disjoint set of cells resulted in classification rates of96%for lymphocytes and96.8%for the diagnostically relevant cells (mesothelial+cells),which includes benign and malign mesothelial cells and metastatic cancer cells. Keywords:Methods:feature extraction,classification and classifier design,quantitative image analysis,evalu-ation and validation,Modalities:microscopic imaging,Diagnostic Task:detection,diagnosis1.INTRODUCTIONTo increase the chance of cure and to reduce curative costs it is desirable to detect cancer as early as possible. Cytopathological methods of diagnosis allow to detect a variety of cancers up to three years ahead of conventional histopathological methods1and,moreover,to prevent unnecessary biopsies.2Specimens of cells can be taken non-or minimally invasive with,e.g.,tiny brushes orfine needle aspiration biopsies(FNABs).This renders cytopathological methods suitable for preventive diagnostics.One of the main drawbacks of cytopathologic diagnostics is the time needed by an expert for the visual inspection of a specimen under a microscope,searching for malignant or suspicious cells and manually selecting them for a detailed analysis.To overcome the problem of Further author information:(Send correspondence to Timna E.Schneider)Timna E.Schneider:E-mail:Timna.Schneider@lfb.rwth-aachen.de,Telephone:+49(241)8027860(a)(b)(c)(d)Figure 1.Different cell types in serous effusions:(a)granulocyte,(b)macrophage,(c)lymphocyte,(d)mesothelial cell.time needed for a diagnosis,the methods have to be automated such that an expert only has to decide whether or not the selected cells are adequate.One essential diagnostic method is DNA image cytometry (DNA-ICM )that measures the amount of DNA in cells based on the Feulgen stain,which stains DNA in the nucleus stoichiometrically.Since cancer mostly is a chromosomal abnormality,it thus can be diagnosed from DNA histograms.The process of standardized DNA-ICM,3based on brightfield microscopical images,is firstly,to manually select about 300nuclei with abnormal morphology,for which the DNA content should be measured,called analysis cells .Secondly a set of about 30normal cell nuclei with a DNA value corresponding to a double chromosomal set (2c)has to be selected as reference cells .This set implicates the integral optical density of the stained cells and their DNA content by computing the ratio of the integral optical density of each nucleus to the mean integral optical density of the reference cells.The decision wether or not a patient has cancer is the third and last step.This decision is achieved by evaluating the position of stemlines of the selected population of analysis cells within a histogram,i.e.,evaluating number and position of modes of the DNA values and by identifying cells with an abnormal DNA value.Trying to automate this method means to automatically acquire images of nuclei,segment them and select the diagnostically relevant ones,i.e.,classify and pre-elect a choice of analysis cells and of reference cells.Current strategies in automating cytopathological diagnostic methods are limited to a special material,on special stains,and on dedicated special imaging systems,4–8such not.For an automated DNA-ICM a machine is available for screening of cervical smears,using a brightfield light microscope.It searches for so-called exceeding events only,i.e.,cells with an abnormal high DNA content,by measuring the DNA value of all existing cells.9But as these cells are rare the same high sensitivity and specificity as with the standardized interactive DNA-ICM,3for which in turn an expert has to select the cells to be measured manually,are not possible.The approach in this paper aims at an algorithmic implementation of such expert knowledge for specimens of serous effusions,i.e.,an automatic detection and selection of analysis cells and reference cells in serous effusions.In these specimens abnormal mesothelial+cells have to be identified as analysis cells and lymphocytes are the preferred type of reference cells to be sought after.Furthermore these cells have to be distinguished from other cell types occurring in these specimens,e.g.,granulocytes and macrophages.Technically speaking a set of mathematical features and a classifier have to be identified to distinguish best between the different types of cells on basis of a manually classified set of cells as a gold standard.Therefore the following sections are organized as follows:In section 2the different cell types are shown and the imaging modality leading to our large database of cell images are described.On these cell images features have to be computed that characterize properties of the different cell types and that are possibly adequate to distinghuish between them.To decide which subset of features discriminates best between the cell types,a feature selection has to be carried out,followed by training and validating classifiers to achieve best classification results for the selected feature subsets.The algorithms we used for this are explained in section 3.Based on the data set and these algorithms we carried out several experiments,described in section 4,trying to find the combination of algorithms that maximizes the overall classification rate.The results (section 5)of the experiments show,that lymphocytes and mesothelial+cells can be discriminated at overall classification rates between 95.4%and 99%.The paper ends up with the conclusions and some future work in section 6.2.MATERIALOur dataset is based on Feulgen stained nuclei from serous effusions with the contours of the nuclei given as chaincodes.An experienced cytopathologist classified 14,346mesothelial+cells,1,915lymphocytes,and 95granulocytes that have been acquired during the DNA-ICM of58specimens.The class of the mesothelial+cells comprises normal,reactive and neoplastic mesothelial cells and metastatic tumor cells.These specimens include2aszites without cancer,due to heart congestion or inflammations and13benign mesotheliomas and14metastatic carcinomas(including9from the lung,2from the ovary,2from the breast and one of an unknown primary tumor).Further from pleural effusions19without cancer,6benign mesotheliomas and4due to metastatic carcinomas(including2from the stomach and2from the pancreas).Some cell types that might occur in serous effusions are shown infigure1.Image acquisition was performed by a brightfield light microscope and a63x oil immersion objective and a three-chip CCD camera with a resulting resolution of≈0.1µm for a pixel edge length.3.METHODSThe generation of a supervised classifier to distinguish between cell types can be split into three parts.Part one:Generation of features,i.e.,to compute a mathematical set of features,which describes the cells.Part two:Feature selection,i.e.,to reduce the computational time needed for a classification of objects and avoid the effect of the peaking phenomenon(degraded performance of a classifier due to a to small number of training samples relative to the the number of features).This means to search for a,depending on the number of training samples,minimal,efficient choice of features to ensure optimal classification results.Part three:Classification and validation,i.e.,to train a classifier according to the chosen features and validating this classifier with a sample set that is disjoint to the training set to rate the quality of this classifier.3.1.Generation of featuresCytopathologists distinguishes between different cell types by considering geometrical properties such as size, shape,characteristics of the nuclear contour and texture of the chromatine structure within the nucleus.On the basis of transmitting light microscopy images of Feulgen stained nuclei and their segmentations,we there-fore extracted112features.This feature collection is motivated by Rodenacker10(Appendices B2to B4)and supplemented with Suk11and comprises some basic geometrical features like area,perimeter,form factor,etc. Additionally we use form describing features based on Fourier descriptors,and on affine invariant10as well as independent affine invariant moments11derived from the central twodimensional polynomial moments,that are computed for the nuclear mask.This feature set is supplemented by texture features.These are the affine invariant and the independent affine invariant moments computed for the extinction image,that is based on the green channel of the original RGB image,and theflat texture image,which is the difference between the extinction image and its medianfiltered version,here using afilter kernel of sizefive for the medianfilter.3.2.Feature selectionTo generate subsets of these features with a good separability performance without testing all possible subsets we chose two feature selection methods.Thefirst is the ranking method,12that iteratively adds that feature, that separates best in the one-dimensional case.As the second method we use the sequential forwardfloating selection(SFFS)as a wrapper-method,13that does not need any parameter.In each step that feature is added to the existing set,that maximizes a separability criterion.After adding the new feature,thefloating back starts. This means that the quality of each subset is tested,that can be formed by removing one feature,such searching for a new combination of size minus one with a higher quality than the previous set of subset size minus one. This is done recursively as long as a smaller feature subset with a higher quality is computed,using the smallest new best feature set as the starting point for adding new features again.Without having knowlegde of the distribution of the classes in each feature space,we use three different objective functions to measure the class separability of each feature set.Fisher’s criterion,that is based on scatter matrices,14selects good feature sets,if the classes have a normal distribution.Mutual information15 uses the likelihood of a feature vector within each class to estimate the density distribution,but a binning of the feature space has to be donefirst to compute the estimate of the distribution.Without considering the distribution of the data within the feature space we use the k-nearest neighbor(kNN)as the third objective function,computing the classification rates with leave-one-out cross-validation on the training set.The kNN hasbeen modified to prevent impasse and,furthermore,to downweight the occurrence of training samples of equal distance and different class memberships.3.3.Classification and validationFor a user,who is not an expert in classification algorithms,a classifier should compute reproducible and comprehensible results,together with a confidence measure for its decisions,that furtheron enables a sorting of the samples according to their conspicuity.These aspects together with the missing knowledge and possibly changing behaviour of the distribution for each new subset of features,led to the use of kNN and Fuzzy-kNN16 to distinguish between the different types of cells.For a kNN we trained a version that makes its decision as soon as k neighbors belong to the same class, providing a kind of rating of the reliability of the decision by considering the number of nearest neighbors belonging to the decision class.A Fuzzy-kNN computes a measure of membership to each class.It takes into account the distances of the sample to its nearest neighbors,thus reducing the influence of nearest neighbors that are far away as well as showing less confidence in almost empty areas in the feature space.As there are different possibilities to weight the distances to the nearest neighbors,we trained the Fuzzy-kNN with two different weighting functions,atfirst,by providing a complete membership of the labeled samples into their own class and nonmembership in all other classes,here denoted as F-kNN-a.Further,we trained it by using their distances to the class mean of that class for which the membership is computed,thus taking into account how typical a cell represents one class,especially in the area between the classes,denoted as F-kNN-b.The weighting function of the F-kNN-b partitions the feature space in areas around each class mean,that contains all points that are closer to this class mean than to any other.The density distribution of the classes therefore should be unimodal with equal variances.For the training of these variants of the kNN we used leave-one-out cross-validation.4.EXPERIMENTSRandomly we selected a set of1,500mesothelial+cells and1,500lymphocytes and we took all95granulocytes available.To compare the influence of the training set size on the classification rate we have generated two different training sets for feature selection and classification,i.e.,the set of the above mentioned1,500lympho-cytes and mesothelial+cells and based on these sets a randomly chosen subset of500mesothelial+cells and lymhocytes each,taking all granulocytes in both training sets.These two training sets contain all112features to compute a combination of a feature subset and a classifier that is suitable for our classification task.Furthermore,we analyze the classification results obtainable with the Euclidean and the Mahalanobis distance respectively.To reduce the computational cost we took the two trainings sets from above and extracted a formerly tested subset of33features,17containing basic geometrical features(area,perimeter,largest inscribable circle, form factor,bending energy)as well as the affine invariant features introduced in Rodenacker.10 This leads to six basic experiments,for which the feature selection and classifier generation has to be carried out:1500all and500all for the training sets containing all features,1500reduced and500reduced for the subset of33features using the Euclidean distance measure,and1500reduced mahal and500reduced mahal for the experiments using the Mahalanobis distance.For each of these six experiments we normalized the feature values to the range[0,1]and computed suitable feature subsets using both the ranking and the SFFS method in combination with each objective function(section 3).We searched for feature subsets up to feature set sizes of40for all features,and up to a set size of20for the reduced initial feature set of33features.Each combination of feature set selection methods and objective functions has been investigated,using k-nearest neighbor objective functions up to k=5.To obtain a classification rate independent of the training set,we validated the best combination of each experiment with k-nearest neighbor and fuzzy k-nearest neighbor for both DNA-ICM relevant types,mesothelial+ cells and lymphocytes,on basis of a set of400samples for both classes,each disjoint to the used training set and up to k=10.As the quality measure for the classifiers we selected the overall classification rate of all classes.Table1.Results of the feature selection for each experiment using the ranking method.Listed are the classification results(g;m;l;t)in percentages for the(g)ranulocytes;(m)esothelial cells;(l)ymphocytes,and the(t)otal rate over all classes,additionally the objective function((m)utual information,the(s)catter matrix criterion,or the(k)NNx with x number of neighbors),the number of features as well as the number of neighbors of the kNN that led to the best classifier of each experiment.experiment classification objective number of number of(g;m;l;t)function features neighbors1500all64.2;97.2;97.8;96.5k1295500all83.2;96.2;96.6;95.3k1421500reduced62.1;96.8;98.1;96.3k1114500reduced78.9;95.8;97.8;95.3k11031500reduced mahal76.8;96.6;97.6;96.5k563500reduced mahal85.3;94.2;97.8;95.1m99Table2.Results of the feature selection for each experiment using SFFS.Listed are the classification results(g;m;l;t) in percentages for the(g)ranulocytes;(m)esothelial cells;(l)ymphocytes,and the(t)otal rate over all classes,additionally the objective function((m)utual information,the(s)catter matrix criterion,or the(k)NNx with x number of neighbors), the number of features as well as the number of neighbors of the kNN that led to the best classifier of each experiment.experiment classification objective number of number of(g;m;l;t)function features neighbors1500all84.2;97.0;97.5;96.8k2122500all88.4;97.6;97.0;96.5k1531500reduced72.6;96.7;98.1;96.6k456500reduced88.4;95.2;97.0;95.4k5341500reduced mahal80.0;96.0;98.3;96.6m75500reduced mahal88.4;94.6;97.4;95.3m445.RESULTSWe identified,eparately for the feature selection methods ranking and SFFS,the classifier with the best total classification rate for each experiment,as is shown in table1for the ranking method and in table2for SFFS. For each experiment this is done by selecting the best classifier among all objective functions,whereas the best classifier of each objective function is identified on the basis of the best total classification rate of a kNN computed for the training paring both tables,it can be seen that the classification results obtained with feature selection based on SFFS are slightly better than those obtained with the ranking method and that the size of the feature subsets is smaller,thus needing less computation time.The resulting feature sets selected as the best during the feature selection on basis of the reduced feature set contain mainly the basic geometrical features(see section3),whereas the features selected over all features mainly comprises a selection of the moment based features.The classifier with the best classification rate on the training data is achieved for1500all using the SFFS method,ing1500mesothelial+cells and lymphocytes each within the training set and searching for suitable feature subsets within the whole set of features(first row in table2).Nevertheless,the results obtained with the reduced set of features for the feature selection show comparable results.The classification results for the validation set are shown in table3and in table4.As can be seen,for kNN and F-kNN-a the classification results for mesothelial+cells and lymphocytes differ slightly from the classification results of the trainings set,but,in total,classifying with the kNN computes the best classification rates.The comparatively weak results of the F-kNN-b show,that the pre-condition of this classifier is not fulfilled, i.e.,this indicates that the two classes have different variances and/or do not only have one mode.Table3.Results of the different kNN variants for eachfilter experiment with k of the best classifier(2).Listed are the classification results(m;l;t)in percentages for the(m)esothelial cells;(l)ymphocytes,and the(t)otal rate over all classes, and additionally the number of features as well as the number of neighbors of the kNN that led to the best classifier of each experiment.experiment kNN F-kNN-a F-kNN-b number of number of(m;l;t)(m;l;t)(m;l;t)features neighbors 1500all97.5;97.5;97.595.5;98.0;96.881.5;96.3;88.9122500all94.0;96.8;95.493.5;95.8;94.677.8;95.5;86.6531500reduced97.5;97.5;97.596.5;98.0;96.881.5;96.3;88.956500reduced95.5;97.3;99.095.5;96.0;95.879.3;96.3;87.8341500reduced mahal97.0;97.0;96.991.8;97.3;94.577.0;93.0;85.075500reduced mahal94.0;97.0;95.549.3;93.0;71.179.5;97.5;88.544Table4.Results of the different kNN variants for each SFFS experiment with k of the best classifier(2).Listed are the classification results(m;l;t)in percentages for the(m)esothelial cells;(l)ymphocytes,and the(t)otal rate over all classes, and additionally the number of features as well as the number of neighbors of the kNN that led to the best classifier of each experiment.experiment kNN F-kNN-a F-kNN-b number of number of(m;l;t)(m;l;t)(m;l;t)features neighbors 1500all95.5;96.8;96.195.5;96.3;95.981.0;99.3;90.1122500all96.8;96.0;96.496.5;96.0;96.377.3;95.0;86.1531500reduced96.5;98.0;97.396.5;98.0;97.375.8;1.0;87.956500reduced99.3;98.8;99.098.0;98.3;98.187.3;98.5;92.9341500reduced mahal95.8;98.0;96.987.3;98.8;93.073.3;84.5;78.975500reduced mahal95.3;98.0;96.788.8;97.5;93.174.5;99.5;87.044Using the Mahalanobis distance as the distance measure does not always increase the classification rate,as can be seen by comparing the results1500reduced mahal with1500reduced and500reduced mahal with500reduced for both training and validation results.During the feature selection process for the experiments using the Mahalanobis distance,some of the feature subsets could not be rated.For these the inverse of the covariance matrix estimate,needed to compute the Mahalanobis distance,could not be computed,as the covariance matrix estimate was singular.For both feature selection and classifier validation,comparing the classification results of each experiment between the two data sets of1500and500cells for lymphocytes and mesothelial+cells each shows,that the kNN nearly always provides lower rates for the smaller sets than for the sets of1500*,but at the same time often needs a smaller number of nearest neigbors and fewer features.This is not always true for the Fuzzy-kNN classifier(see table4),which indicates that the density of the training data of both classes around the samples to be classified is high.Therefore the distance weighting of the Fuzzy-kNN variants,which are a kind of is nearer to class x,cannot enhance the classification result.Therefore concentrating on the classification rates of the kNN for the validation set results in a maximal difference of5.3%percentage points over all experiments for the differences of the classification rates of each class as well as for the overall classification rate and in classification rates nearly always above95%.As an example some classification results of the classifier1500all of table2are shown infigure2for the lymphocytes and infigure3for the mesothelial+cells.As an oversight of relevant cells may result in a false-negative diagnosis,i.e.the cancer will not be detected, one should choose that classifier that includes most analysis cells.Minor missclassifications of reference cells will not affect the diagnostic result,as only the mean value of their optical densities will be taken.Therefore takingthe classifier with the highest classification rate for the mesothelial+cells after the training process(500all in table2)results for the validation cell set in classification rates of96.8%for the mesothelial+cells and96%for the lymphocytes.Repeating feature selection and training of the classifiers based on the classification rates of the mesothelial+cells might enhance the detection rate of these cells,thus reducing the risk of a false-negative diagnosis.But,so far,it can be stated that the current results already provide an encouraging basis to distinguish between lymphocytes and mesothelial+cells.6.CONCLUSIONS AND FUTURE WORKThe results of F-kNN-b and a classifier using the Mahalanobis distance confirm,that the choice of non-parametric classifiers was the right one.Especially during the process of feature selection,good feature combinations are not necessarily independent,nor normally distributed.Since computing the Mahalanobis distance also increases the time needed for computation,this should not be used during the feature selection,but it is an alternative to possibly enhance the resulting classifier.Comparing the results of the different experiments led to comparable classification results for different feature sets and different classifiers,thus offering the choice to choose a classifier that is a compromise of the number of nearest neighbors,the number of features,and then to combine it with a rating of the reliability of the decision.Depending on the application,the strategy to rate the classifiers according to their overall classification result can be modified to choose these classifiers that achieves best classification results for one class.But this may sometimes lead to really worse rates for other classes.So,with our strategy to select the classifiers according to the best overall classification rate we could show,that with the kNN the obtainable classification rates of each class as well as the overall rate are stable and at a hight rate and comparable for lymphocytes and mesothelial+ cells.As the next step,the classifiers have to be tested under routine conditions,i.e.,try to identify lymphocytes and mesothelial+cells among automatically segmented objects.This might possibly lead to the necessity of collecting prototypes of granulocytes,and macrophages,as well as artefact segmentations or lytic cells.Furthermore,within the set of mesothelial+cells a rating has to be introduced to provide the cytopathologist with a pre-election of cells being most suspicious of cancer.However,overall we could show that it is possible to distinguish mesothelial+cells and lymphocytes within a manually classified set of cells from serous effusions at a rate of96.8%and96%respectively.As these are the relevant classes for the DNA-ICM of serous effusion,this provides a promising approach to distinguish these cells within automatically segmented objects of a whole specimen.This way an automation of DNA-ICM for serous effusion becomes feasible.ACKNOWLEDGMENTSThe project is supported by the Viktor-and-Mirka-Pollak-Fund for Biomedical Engineering.。

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天门灵光摄精术
作者:互联网来源:互联网发布日期:2006-10-17 点击次数: 12
防治元阳漏泄的仙家强身秘诀
站、坐皆可,身体端正,闭目,自然呼吸,随意揉捏双手十指,揉捏力度以手指略感疼痛为宜,十根手指都要捏遍,同时叩齿百遍,完成之后即双手合掌,高举于头顶之上。

然后想象大脑中有持续的“嗡——”声,头顶内部有白色光明,保持七呼吸以上即可,最后用左手中指上下揉搓印堂穴处二十一次(此法名为“天门灵光摄精术”,单用此法即可防治精关不固、梦遗滑精等走失元阳之病症,其功效显著,可谓胜于药石。

世间不少修炼者都会遇到“精满自溢”和“气冲下元”等所导致的梦遗滑精之类走失元阳之气的情况,往往使多日积累的修炼境界功亏一篑,由于没有好的对治方法,反复如此,使修炼长期在原地踏步,达不到“集精累气以成真”的目的。

如果运用天门灵光摄精术,此类烦恼即可烟消云散,修炼自然容易上到新的台阶。

常人于临睡前行持此法,只需要几分钟的时间,即可获得防治梦遗滑精之类病症的
效果,每天练习两遍以上更能见其显效,此法对脑力工作者和青少年学生等人群也是健脑益智的灵丹妙药,不但可以防治头晕头痛,长期练习还能增强体质,提高免疫力,这是因为此法能够在一定程度上减少周期性性生理系统活动所消耗的生命能,具体也就是将用于给精子“充电”之类的能量在“电源”处便引入其他的“线路”之中,使无意义的耗能被抑制,而需要能量的系统则可以获得更多的支持,从而可以强化很多有用的功能。

练习此术,只要不着意强化导引便不会对生育能力造成影响,如果不是为了获取修炼的超常成就,便不必每天习练此术,可间隔一至七天练习一次,这样就更不可能影响生育能力了,而仍然可以在一定程度上获得其良好功效……
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