Medical image analysis has become a challenging task as it contributes to disease diagnosis. Breast cancer has been the prominent reason for death among women. While analysing mammogram images, there is a need for clear differentiation of between benign and malignant tissues. Also, early detection of breast masses lead to prediction of breast cancer at the initial stage and minimizes risk of death. In this work, the image is preprocessed using Median filter and is segmented using Fuzzy C Means clustering. Fuzzy C-Means clustering algorithm helps in extracting the region of interest by allocating pixels with similar characteristics into a single group. A pixel may be present in various clusters with different membership values. The belongingness of a pixel to a cluster is decided by the highest membership value. Then the statistical, texture and shape features are extracted from the image. Since there may be many features that are less relevant for classification process, prominent features are selected with the help of Binary Dragonfly Optimization Algorithm and the selected features are fed into a Feed Forward Neural Network trained with Back Propagation Learning to classify the mass as benign or malignant. Experiments are conducted over 320 images from mini-MIAS database out of which 200 ROIs are used in training and 120 ROIs are used in testing phase. The region of interest from given mammogram images are extracted successfully and classified with an accuracy of 98.75%.

医学图像分析已成为一项具有挑战性的任务，因为它有助于疾病诊断。乳腺癌一直是女性死亡的主要原因。在分析乳房 X 线照片时，需要清楚地区分良性和恶性组织。此外，早期发现乳房肿块可以在初期预测乳腺癌并将死亡风险降至最低。在这项工作中，图像使用中值滤波器进行预处理，并使用模糊 C 均值聚类进行分割。模糊 C 均值聚类算法通过将具有相似特征的像素分配到单个组中来帮助提取感兴趣的区域。一个像素可能存在于具有不同隶属度值的各种集群中。像素对簇的归属由最高的隶属度值决定。然后统计，从图像中提取纹理和形状特征。由于可能有许多与分类过程不太相关的特征，因此在二进制蜻蜓优化算法的帮助下选择突出的特征，并将所选特征馈送到经过反向传播学习训练的前馈神经网络，以将质量分类为良性或恶性的。对来自 mini-MIAS 数据库的 320 多幅图像进行了实验，其中 200 个 ROI 用于训练，120 个 ROI 用于测试阶段。从给定的乳房 X 线照片图像中成功提取感兴趣区域，并以 98.75% 的准确度进行分类。在二进制蜻蜓优化算法的帮助下选择突出的特征，并将选择的特征馈送到经过反向传播学习训练的前馈神经网络，以将质量分类为良性或恶性。对来自 mini-MIAS 数据库的 320 多幅图像进行了实验，其中 200 个 ROI 用于训练，120 个 ROI 用于测试阶段。从给定的乳房 X 线照片图像中成功提取感兴趣区域，并以 98.75% 的准确度进行分类。在二进制蜻蜓优化算法的帮助下选择突出的特征，并将选择的特征馈送到经过反向传播学习训练的前馈神经网络，以将质量分类为良性或恶性。对来自 mini-MIAS 数据库的 320 多幅图像进行了实验，其中 200 个 ROI 用于训练，120 个 ROI 用于测试阶段。从给定的乳房 X 线照片图像中成功提取感兴趣区域，并以 98.75% 的准确度进行分类。