Automated mammogram image segmentation is one of the most important methods in the domain of medical diagnosis and decision systems. Accurate segmentation of mammogram plays a key role for the detection of any kind of abnormality like lesion tissues, cyst in mammogram images for medical diagnosis. In this study, a novel hybrid soft computing entrenched segmentation method for mammograms is introduced for the detection of breast cancer in early stages. Here, we have designed a novel automatic mammogram segmentation method using intuitionistic fuzzy soft sets (IFSS) and Multigranulation rough set. First, the proposed clustering algorithm accomplishes a soft information structure from the source image using IFSSs via multiple fuzzy membership functions with Yager generating function. The IFSS handles the ambiguity among lesion and non-lesion pixels through the hesitation degree while shaping the membership function. To reduce distant pixels which do not belong to the region of interest (ROI), the lesion tissues in mammogram image is segregated by decision making scheme via a rough approximation of a fuzzy concept under the field of multigranulation space. Later, the proposed scheme utilizes soft-information builder with accuracy and roughness scores on multigranulation approximation space for segregation of normal and abnormal (lesion tissues) pixel from mammograms. The proposed model has generated a threshold image from accuracy and roughness scores via fuzzy defuzzification. The proposed segmentation model performs better than the existing methods using evaluation metrics like segmentation accuracy, Jeccards similarity coefficient.

自动化的乳房X射线照片图像分割是医学诊断和决策系统领域中最重要的方法之一。乳房X线照片的正确分割对于检测任何类型的异常（如病变组织，乳房X射线照片中的囊肿）以进行医学诊断起着关键作用。在这项研究中，针对乳腺X线照片的一种新型混合软计算固定分割方法被介绍用于早期乳腺癌的检测。在这里，我们设计了一种使用直觉模糊软集（IFSS）和Multigranulation粗糙集的新颖的自动乳房X线照片分割方法。首先，所提出的聚类算法通过具有Yager生成函数的多个模糊隶属函数，使用IFSS从源图像完成软信息结构。IFSS通过影响隶属度的犹豫程度来处理病变像素和非病变像素之间的歧义。为了减少不属于感兴趣区域（ROI）的遥远像素，在多颗粒空间领域下，通过模糊概念的粗略近似，通过决策方案将乳房X线照片中的病变组织分离。后来，提出的方案在多颗粒近似空间上利用具有准确性和粗糙度得分的软信息构建器，从乳房X线照片中分离出正常和异常（病变组织）像素。所提出的模型已经通过模糊去模糊从精度和粗糙度得分中生成了阈值图像。提出的细分模型比使用评估指标（如细分准确性，