Detection of Lesion Boundaries and Area in Breast Magnetic Resonance Imaging

Çetinel, Gökçen; Gül, Sevda

doi:10.16984/saufenbilder.322604

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…A circle shaped object has a circularity of unity, oblong-shaped objects possess a circularity less than 1 [20]. In this experiment, to evaluate and compare the performance of the proposed study with presented studies, proposed method is applied to the breast lesion image given in [21]. In like manner, original image, RG and snake applied images are illustrated in Figures 8 and 9, respectively.…”

Section: Bit-quad Methodsmentioning

confidence: 99%

Breast Lesion Segmentation and Area Calculation for MR Images

Çetinel¹,

Gül²

2018

IJIGSP

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In this paper, our goal is to determine the boundaries of lesion and then calculate the area of existing lesion in breast magnetic resonance (MR) images to provide a useful information to the radiologists. For this purpose, at first stage region growing (RG) method and active contour model (Snake) is applied to the images to make the boundaries of lesion visible. RG method is one of the simplest approaches for image segmentation and provides accurate results with lower computation time due to its seed point initialization step. Snake method molds a closed contour to the boundary of a region in an image and is also popular in medical image segmentation studies. In the presented study, both of these methods are utilized to determine the lesion boundaries. After determining the boundaries of lesion accurately in the second stage of the study, bit-quad method is applied to the segmented images. Bit quad method is used to compute the area and perimeter of binary lesion images based on matching the logical state of regions of image to binary patterns. Finally, to evaluate the performance of the proposed study, computer simulations are performed. It is demonstrated via computer simulations that the lesion area and parameter values are very close to real values. By means of this study it is aimed to support radiologists during diagnosis and assessment of breast lesions.

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Section: Bit-quad Methodsmentioning

confidence: 99%

Breast Lesion Segmentation and Area Calculation for MR Images

Çetinel¹,

Gül²

2018

IJIGSP

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Detection of Lesion Boundaries and Area in Breast Magnetic Resonance Imaging

Çetinel

Gül

2018

Sakarya University Journal of Science

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ÖZBu çalışmada, meme kanserinin teşhisinde yaygın olarak kullanılan modalitelerden biri olan MRG sisteminden elde edilen görüntüler kullanılarak memede oluşan lezyonların sınırlarının belirlenmesi ve lezyon alanının hesaplanmasına yönelik bir sistem geliştirilmiştir. Geliştirilen sistem, radyologlara büyük kolaylıklar sağlayan ve birçok değiştirilebilir seçenek sunan bir ara yüz üzerinden tasarlanmıştır. Lezyon sınırlarının belirlenmesi ve alanının optimum şekilde hesaplanması için çalışmada dört farklı yöntemden yararlanılmaktadır. Bu yöntemler, eşikleme tabanlı (Otsu eşikleme yöntemi), bulanık mantık tabanlı (bulanık c-ortalama (Fuzzy c-means, FCM)), bölge büyütme tabanlı (Region Growing, RG) ve kümeleme tabanlı (k-ortalama) segmentasyon yöntemlerdir. Otsu, FCM ve RG yöntemleri tek kanallı gri-seviye bölütleme yöntemleridir. K-ortalama yöntemi ise, üç-kanallı renkli görüntüde doğrudan kullanılabilen bir bölütleme yöntemidir. Segmentasyon adımdan sonra, lezyon alanının hesaplanması için bit-dörtlüsü (bitquad) yöntemi uygulanmıştır. Bu aşamalar gerçekleştirildikten sonra geliştirilebilir bir hastane otomasyon sistemi tasarlanmıştır. Tasarlanan sistem uzmana görsel olarak farklı seçenekler sunarak meme lezyonlarını birçok yönden inceleme imkânı sağlamaktadır.Anahtar Kelimeler: Meme kanseri, Manyetik rezonans görüntüleme, segmentasyon, bit dörtlüsü yöntemi. ABSTRACTIn this study, we have developed a system for determining the boundaries of the lesions which come into existence in the breast and calculating the lesion area by using images obtained from the MRI system, which is one of the modalities widely used in diagnosis of the breast cancer. The developed system is designed with an interface that provides great convenience to the radiologists and offers many interchangeable options. In order to determine the boundary of the lesion and to calculate the area optimally, in this study four different methods are utilized. These methods are thresholding based (Otsu thresholding method), fuzzy logic based (fuzzy c-means, FCM), region growing based (Region Growing, RG) and cluster-based (k-means) segmentation methods. The Otsu, FCM and RG methods are single-channel gray-level segmentation methods. In case, the k-means method is a method of segmentation that can be used directly

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Bilgisayarlı Tomografi Görüntüleri Üzerinde Karaciğer Bölgesinin Bilgisayar Destekli Otomatik Bölütleme Uygulaması

Dandıl

2019

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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In this study, an automated computer-assisted method for the segmentation of the liver on abdominal computed tomography (CT) images is proposed and an application software is developed for segmentation. The segmentation process is performed by using Region Growing (RG) and Fuzzy C-Means (FCM) methods by means of the developed application software. The similarities of segmentation results obtained by RG and FCM algorithms and the liver regions determined by the physician are measured quantitatively using the application software. As a result of experimental studies, RG algorithm is more successful than FCM algorithm both in terms of similarity rates and statistical results. Figure A. Liver segmentation procedure on CT scans using proposed method Purpose: The liver is a vital organ in the body. In liver-related diseases, especially CT scans, the delineation of the liver is essential for diagnosis and treatment planning. In order to improve the accuracy of the segmentation of liver, a computer-assisted method based on RG and FCM and an application software are proposed on abdominal CT images in this paper. Theory and Methods: For the segmentation of the liver region, RG and FCM algorithms are used. In order to measure the performance of segmentation with these algorithms, a physician marked out the boundary of the liver. In the study, the segmentation results obtained by the RG and FCM algorithms are compared on the developed application software using physician selection criteria. Jaccard similarity criterion are used to compare segmentation results. Results: In the experimental studies on 88 CT images, average performance values are obtained for 91.15% in the RG algorithm and 75.16% in the FCM algorithm according to the Jaccard similarity criterion. In computational times, the RG algorithm is more successful than the FCM algorithm, measured on average from 3.15 seconds to 7.50 seconds. Conclusion: In the study, segmentation with the RG algorithm is more successful. In addition, the statistical significance of quantitative values obtained from similarity measurements is measured. It is concluded that the segmentation results obtained by the RG algorithm revealed a significant difference by evaluating the significance tests. Moreover, the segmentation process times are compared with both segmentation methods and segmentation with RG is found to be faster. The findings show that the proposed method can be used as a secondary tool in the decision-making process of physicians.

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Detection of Lesion Boundaries and Area in Breast Magnetic Resonance Imaging

Cited by 3 publications

References 14 publications

Breast Lesion Segmentation and Area Calculation for MR Images

Breast Lesion Segmentation and Area Calculation for MR Images

Detection of Lesion Boundaries and Area in Breast Magnetic Resonance Imaging

Bilgisayarlı Tomografi Görüntüleri Üzerinde Karaciğer Bölgesinin Bilgisayar Destekli Otomatik Bölütleme Uygulaması

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