{"title":"基于直方图分类的皮肤癌图像自动aaa分割","authors":"Dr.Ahlam Fadhil Mahmood, Hamed A. Mahmood","doi":"10.33899/RENGJ.2015.108994","DOIUrl":null,"url":null,"abstract":"Skin cancer has been the most common and represents 50% of all new cancers detected each year. If detected at an early stage, simple and economic treatment can cure it mostly. Accurate skin lesion segmentation is critical in automated early diagnosis system. This paper present a triple segmentation procedure based on the pixels distribution Bell-shaped (Normal), J-shaped, Reverse J-shaped and U-shaped peaks that is bimodal. According to the nature of dermoscopy images distributions, three segmentation methods are used to identify the normal skin cancer from malignant skin and to extract the tumor region. First, active contours are used for bell distribution shape. Second segmentation is done using adjusted ant colony optimization when the Ushaped peaks distribution was classify. Third segmentation strategies apply adaptive threshold for two J-shapes. Experiments on synthetic and real dermoscopy images demonstrate the advantages of the proposed methods that is able to produce ant colony optimization accurate segmentation when applied to a large number of skin cancer (melanoma) images. Keyword: Segmentation dermoscopy images, ant colony, active contours, adaptive threshold, Histogram يثلاث عاطقتسا ىلع دامتعاب دلجلا ناطرسل يللآا أ عیزوتلا فنص د دومحم لضاف ملاحأ. دومحم زیزعلا دبع دماح Ahlam.mahmood@gmail.com hamedce43@gmail.com بوساحلا ةسدنھ مسق ةعماج لصوملا ا خل لا ص ة عاونأ رثكأ نم دلجلا ناطرس دعی .ایونس تاناطرسلل ةصخشملا تلااحلا فصن لكشی ذإ اراشتنا تاناطرسلا نأ ثیحو ایبسن ةبعص دلجلا نم باصملا ءزجلا عاطقتسا ةیلمع ربتعت .لقأ ھفلكبو ملسأ ھجلاع لعجی ةركبملا لحارملا يف ھفاشتكا يللآا صیخشتلا ةمظنلأ ،سرجلا عیزوت ةروصلا رصانع عیزوت بسح يلأ يثلاث عاطقتسا حرتقت ةقرولا هذھ .ركبملا ھسوكعم و يج فرحلا لكشب عیزوت ھ قیرط ثلاث مادختسا مت ةیرھجملا روصلا تاعیزوت بسح .ةمقلا يئانث عیزوت وا ملا ةقیرط ،لاوأ .مرولا ةقطنم جارختسلال ةباصملاو ةیعیبطلا قطانملا نیب لصفلل لاعفلا طیح اھعیزوت فنصملا روصلل فرح لكش ىلع روصلا عیزوت نوك ةلاح يف لمنلا ةرمعتسم ةیلثمأ مادختساب هزاجنا مت عاطقتسا يناث .سرج لكش ىلع ةدئاف تدكأ ةیرھجملا روصلا تدمتعا يتلا براجتلا .يج فرحلا يلكشل ةلاعفلا ةبتعلا يھ عاطقتسا ةیجیتارتسإ ثلاث .وی حرتقملا قرطلا .قیقد عاطقتسا يف اھتیناكملإ ة Received: 15 – 4 2015 Accepted: 28 – 11 2015 Al-Rafidain Engineering Vol. 23 No. 5 December 2015 32","PeriodicalId":339890,"journal":{"name":"AL Rafdain Engineering Journal","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Automatic Triple-A Segmentation of Skin Cancer Images based on Histogram Classification\",\"authors\":\"Dr.Ahlam Fadhil Mahmood, Hamed A. Mahmood\",\"doi\":\"10.33899/RENGJ.2015.108994\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Skin cancer has been the most common and represents 50% of all new cancers detected each year. If detected at an early stage, simple and economic treatment can cure it mostly. Accurate skin lesion segmentation is critical in automated early diagnosis system. This paper present a triple segmentation procedure based on the pixels distribution Bell-shaped (Normal), J-shaped, Reverse J-shaped and U-shaped peaks that is bimodal. According to the nature of dermoscopy images distributions, three segmentation methods are used to identify the normal skin cancer from malignant skin and to extract the tumor region. First, active contours are used for bell distribution shape. Second segmentation is done using adjusted ant colony optimization when the Ushaped peaks distribution was classify. Third segmentation strategies apply adaptive threshold for two J-shapes. Experiments on synthetic and real dermoscopy images demonstrate the advantages of the proposed methods that is able to produce ant colony optimization accurate segmentation when applied to a large number of skin cancer (melanoma) images. 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Automatic Triple-A Segmentation of Skin Cancer Images based on Histogram Classification
Skin cancer has been the most common and represents 50% of all new cancers detected each year. If detected at an early stage, simple and economic treatment can cure it mostly. Accurate skin lesion segmentation is critical in automated early diagnosis system. This paper present a triple segmentation procedure based on the pixels distribution Bell-shaped (Normal), J-shaped, Reverse J-shaped and U-shaped peaks that is bimodal. According to the nature of dermoscopy images distributions, three segmentation methods are used to identify the normal skin cancer from malignant skin and to extract the tumor region. First, active contours are used for bell distribution shape. Second segmentation is done using adjusted ant colony optimization when the Ushaped peaks distribution was classify. Third segmentation strategies apply adaptive threshold for two J-shapes. Experiments on synthetic and real dermoscopy images demonstrate the advantages of the proposed methods that is able to produce ant colony optimization accurate segmentation when applied to a large number of skin cancer (melanoma) images. Keyword: Segmentation dermoscopy images, ant colony, active contours, adaptive threshold, Histogram يثلاث عاطقتسا ىلع دامتعاب دلجلا ناطرسل يللآا أ عیزوتلا فنص د دومحم لضاف ملاحأ. دومحم زیزعلا دبع دماح Ahlam.mahmood@gmail.com hamedce43@gmail.com بوساحلا ةسدنھ مسق ةعماج لصوملا ا خل لا ص ة عاونأ رثكأ نم دلجلا ناطرس دعی .ایونس تاناطرسلل ةصخشملا تلااحلا فصن لكشی ذإ اراشتنا تاناطرسلا نأ ثیحو ایبسن ةبعص دلجلا نم باصملا ءزجلا عاطقتسا ةیلمع ربتعت .لقأ ھفلكبو ملسأ ھجلاع لعجی ةركبملا لحارملا يف ھفاشتكا يللآا صیخشتلا ةمظنلأ ،سرجلا عیزوت ةروصلا رصانع عیزوت بسح يلأ يثلاث عاطقتسا حرتقت ةقرولا هذھ .ركبملا ھسوكعم و يج فرحلا لكشب عیزوت ھ قیرط ثلاث مادختسا مت ةیرھجملا روصلا تاعیزوت بسح .ةمقلا يئانث عیزوت وا ملا ةقیرط ،لاوأ .مرولا ةقطنم جارختسلال ةباصملاو ةیعیبطلا قطانملا نیب لصفلل لاعفلا طیح اھعیزوت فنصملا روصلل فرح لكش ىلع روصلا عیزوت نوك ةلاح يف لمنلا ةرمعتسم ةیلثمأ مادختساب هزاجنا مت عاطقتسا يناث .سرج لكش ىلع ةدئاف تدكأ ةیرھجملا روصلا تدمتعا يتلا براجتلا .يج فرحلا يلكشل ةلاعفلا ةبتعلا يھ عاطقتسا ةیجیتارتسإ ثلاث .وی حرتقملا قرطلا .قیقد عاطقتسا يف اھتیناكملإ ة Received: 15 – 4 2015 Accepted: 28 – 11 2015 Al-Rafidain Engineering Vol. 23 No. 5 December 2015 32