Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE)

Levent CİVCİK, (Selçuk Üniversitesi, Mühendislik Mimarlık Fakültesi, Elektrik Elektronik Mühendisliği Bölümü, Konya, Türkiye)
Burak YILMAZ, (Selçuk Üniversitesi, Mühendislik Mimarlık Fakültesi, Elektrik Elektronik Mühendisliği Bölümü, Konya, Türkiye)
Yüksel ÖZBAY, (Selçuk Üniversitesi, Mühendislik Mimarlık Fakültesi, Elektrik Elektronik Mühendisliği Bölümü, Konya, Türkiye)
Ganime Dilek EMLİK (Necmettin Erbakan Üniversitesi, Meram Tıp Fakültesi, Radyoloji, Anabilim Dali, Konya, Türkiye)
Turkish Journal of Electrical Engineering and Computer Sciences
2 0
Yıl: 2015 Cilt: 23 Sayı: 3 Sayfa Aralığı: 853 - 872 Metin Dili: İngilizce İndeks Tarihi: 29-07-2022

Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE)

Öz:
Abstract: Microcalcification detection is a very important issue in early diagnosis of breast cancer. Generally physicians use mammogram images for this task; however, sometimes analyzing these images become a hard task because of problems in images such as high brightness values, dense tissues, noise, and insufficient contrast level. In this paper, we present a novel technique for the task of microcalcification detection. This technique consists of three steps. The first step is focused on removing pectoral muscle and unnecessary parts from the mammogram images by using cellular neural networks (CNNs), which makes this a novel process. In the second step, we present a novel image enhancement technique focused on enhancing lesion intensities called the automated lesion intensity enhancer (ALIE). In the third step, we use a special CNN structure, named multistable CNNs. After applying the combination of these methods on the MIAS database, we achieve 82.0% accuracy, 90.9% sensitivity, and 52.2% specificity values.
Anahtar Kelime:

Konular: Mühendislik, Elektrik ve Elektronik
Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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APA CİVCİK L, YILMAZ B, ÖZBAY Y, Emlik G (2015). Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE). , 853 - 872.
Chicago CİVCİK LEVENT,YILMAZ BURAK,ÖZBAY Yüksel,Emlik Ganime Dilek Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE). (2015): 853 - 872.
MLA CİVCİK LEVENT,YILMAZ BURAK,ÖZBAY Yüksel,Emlik Ganime Dilek Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE). , 2015, ss.853 - 872.
AMA CİVCİK L,YILMAZ B,ÖZBAY Y,Emlik G Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE). . 2015; 853 - 872.
Vancouver CİVCİK L,YILMAZ B,ÖZBAY Y,Emlik G Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE). . 2015; 853 - 872.
IEEE CİVCİK L,YILMAZ B,ÖZBAY Y,Emlik G "Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE)." , ss.853 - 872, 2015.
ISNAD CİVCİK, LEVENT vd. "Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE)". (2015), 853-872.
APA CİVCİK L, YILMAZ B, ÖZBAY Y, Emlik G (2015). Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE). Turkish Journal of Electrical Engineering and Computer Sciences, 23(3), 853 - 872.
Chicago CİVCİK LEVENT,YILMAZ BURAK,ÖZBAY Yüksel,Emlik Ganime Dilek Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE). Turkish Journal of Electrical Engineering and Computer Sciences 23, no.3 (2015): 853 - 872.
MLA CİVCİK LEVENT,YILMAZ BURAK,ÖZBAY Yüksel,Emlik Ganime Dilek Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE). Turkish Journal of Electrical Engineering and Computer Sciences, vol.23, no.3, 2015, ss.853 - 872.
AMA CİVCİK L,YILMAZ B,ÖZBAY Y,Emlik G Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE). Turkish Journal of Electrical Engineering and Computer Sciences. 2015; 23(3): 853 - 872.
Vancouver CİVCİK L,YILMAZ B,ÖZBAY Y,Emlik G Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE). Turkish Journal of Electrical Engineering and Computer Sciences. 2015; 23(3): 853 - 872.
IEEE CİVCİK L,YILMAZ B,ÖZBAY Y,Emlik G "Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE)." Turkish Journal of Electrical Engineering and Computer Sciences, 23, ss.853 - 872, 2015.
ISNAD CİVCİK, LEVENT vd. "Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE)". Turkish Journal of Electrical Engineering and Computer Sciences 23/3 (2015), 853-872.
Sayfa Oluşturulma Tarihi
17-04-2024 2:40

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