Aravali Pharma and Lifesciences, New Delhi, 110075, India.
J Digit Imaging. 2021 Apr;34(2):428-439. doi: 10.1007/s10278-021-00444-3. Epub 2021 Mar 23.
Breast cancer is one of the leading causes of mortality in the world and it occurs in high frequency among women that carries away many lives. To detect cancer, extraction or segmentation of lesions/tumors is required. Segmentation process is very crucial if the mammogram images are blurred or low contrast. This paper suggests a novel clustering approach for segmenting lesions/tumors in the mammogram images using Atanassov's intuitionistic fuzzy set theory. The algorithm initially converts an image to an intuitionistic fuzzy image using a novel intuitionistic fuzzy generator. From the intuitionistic fuzzy image, two membership intervals are computed. Then, using Zadeh's min t-conorm, a new membership function is computed. Using the new membership function, an interval type 2 fuzzy image is constructed. Two types of distance functions are used in clustering-intuitionistic fuzzy divergence and a fuzzy exponential type distance function. Further, in each iteration, membership matrix is updated using a hesitation degree and a clustered image is obtained. Tumors/lesions are then segmented from the clustered image. The proposed method is compared with existing methods both quantitatively and qualitatively and it is observed that the proposed method performs better than the existing methods.
乳腺癌是全球主要死因之一，在女性中发病率较高，夺走了许多生命。为了检测癌症，需要提取或分割病变/肿瘤。如果乳房 X 光图像模糊或对比度低，则分割过程非常关键。本文提出了一种使用 Atanassov 的直觉模糊集理论对乳房 X 光图像中的病变/肿瘤进行分割的新聚类方法。该算法首先使用一种新的直觉模糊生成器将图像转换为直觉模糊图像。从直觉模糊图像中，计算出两个隶属度区间。然后，使用 Zadeh 的 min t-合，计算出新的隶属函数。使用新的隶属函数，构建了一个区间型 2 模糊图像。聚类中使用了两种距离函数-直觉模糊分歧和模糊指数型距离函数。进一步，在每次迭代中，使用犹豫度更新隶属度矩阵，并获得聚类图像。然后从聚类图像中分割肿瘤/病变。该方法在定量和定性方面与现有方法进行了比较，结果表明该方法优于现有方法。
