توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Classification of Breast Lesions in Dynamic Contrast–Enhanced MR Images Using Multiple Layer Perceptron and Fuzzy Neural Network
اطلاعات انتشار: هفدهمین کنفرانس مهندسی پزشکی ایران، سال
تعداد صفحات: ۴
In recent years, the development of computer–aided diagnosis (CAD) for breast MR image (MRI) has been a bigchallenge. Usually multiple layer perceptron (MLP) was used for classification of breast MRI lesions. Fuzzy technique can integrate human expert’s knowledge into the system and integrating it with artificial neural network (ANN) could provide us with more intelligent systems. Therefore, in this work, a threelayer feed–forward MLP classifier and a four–layer feed–forward fuzzy neural network (FNN) classifier were used separately to compare their diagnostic performance in discrimination between malignant and benign breast lesions. This work included 40 (23 malignant and 17 benign) histopathologically proven lesions and the steps of this work were as follows: region of interest (ROI) selection, fuzzy c–means (FCM) segmentation, some morphological feature extraction, MLP and FNN classifications, Receiver Operating Characteristic (ROC) analysis. The results showed FNN classifier has a better diagnostic performance than MLP classifier in discrimination between malignant and benign lesions, because FNN classifier has a greater accuracy and area under the receiver operating characteristic curve (AUC) than MLP classifier, and also at the similar sensitivity, FNN classifier has a greater specificity than MLP classifier. This indicates FNN could provide us with good performance in discrimination between malignant and benign breast lesions which can lead to more powerful breast MRI CADs.<\div>

۲Gradient Vector Flow Snake Segmentation of Breast Lesions in Dynamic Contrast–Enhanced MR Images
نویسنده(ها): ، ،
اطلاعات انتشار: هفدهمین کنفرانس مهندسی پزشکی ایران، سال
تعداد صفحات: ۴
The development of computer–aided diagnosis (CAD) for breast magnetic resonance (MR) images has encountered some big challenges. One of these challenges is related to breast lesion segmentation. Accurate segmentation of breast lesions has a vital role in other consequent applications such as feature extraction. Since malignant breast lesions typically appear with irregular borders and shapes in MR images whereas benign masses appear with more regular shapes, and smooth and lobulated borders, it seems that the accurate segmentation ofbreast lesion borders in MR images are important. To achieve this purpose, we have used the Gradient Vector Flow (GVF) snake segmentation method. This study included 52(33 malignant and 19 benign) histopathologically proven breast lesions and the stages of the proposed method are as follows: selecting the region of interest (ROI), segmentation using GVF, evaluation of GVF snake segmentation method. The results of GVF segmentation method in this study were satisfactory referred to the radiologist’s manual segmentation. The results showed the GVF snake segmentation method correctly segmented 97% of malignant lesion borders and 89.5% of benign lesion borders at the overlap threshold of 0.6. This indicates GVF snake segmentation method could provide us with a powerful method that can make an accurate segmentation in breast lesion borders.<\div>
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