For classification of tumors in mammography, the major features are extracted from the segmented tumor. However, some details of the tumor margin, such as the spiculated parts, are eliminated in the segmentation step. The current study suggests a new approach for extracting the spiculated parts and tumor core. The proposed method segments the tumor by assessing the similarity of the pixels of the tumor core and dissimilarity of the spiculated parts. Then, the spiculated parts and the tumor core are combined to create the final segmentation. Next, the statistical features and fractal dimensions are extracted from the tumor. The fractal dimension is a measure of complexity of the tumor shape that is effective for discriminating between benign and malignant tumors. The simulation results show that the proposed method is more suitable than other methods. The area under the ROC curve and the accuracy of the proposed method on mini-MIAS were 0.9627 and 89.66% and for DDSM were 0.9777 and 93.50%, respectively. The results confirm the efficiency of the proposed method for extracting the mass core and spiculated parts. They also show that use of the fractal dimension increases the accuracy of classification and complements the other shape features.

为了在乳腺X线摄影中对肿瘤分类，从分割的肿瘤中提取主要特征。然而，在分割步骤中消除了一些肿瘤边缘的细节，例如针状部分。当前的研究提出了一种新的方法来提取加香的部分和肿瘤核心。所提出的方法通过评估肿瘤核心像素的相似性和针状部分的相似性来分割肿瘤。然后，将网状部分和肿瘤核心结合在一起以形成最终的分割。接下来，从肿瘤中提取统计特征和分形维数。分形维数是衡量肿瘤形状复杂程度的有效方法，可有效区分良性和恶性肿瘤。仿真结果表明，该方法比其他方法更合适。在mini-MIAS上，ROC曲线下的面积和所提方法的精度分别为0.9777和89.66％，而对于DDSM，该方法的准确性为93.50％。结果证实了所提出的提取质量核和细小零件的方法的效率。他们还表明，使用分形维数可以提高分类的准确性，并补充其他形状特征。