Segmentation of ultrasonic breast tumors based on homogeneous patch

• Published in: Medical physics (Lancaster) Vol. 39; no. 6; pp. 3299 - 3318
• Main Authors: Gao, Liang, Yang, Wei, Liao, Zhiwu, Liu, Xiaoyun, Feng, Qianjin, Chen, Wufan
• Format: Journal Article
• Language: English
• Published: United States American Association of Physicists in Medicine 01.06.2012
• Subjects: Algorithms; Analysis of texture; biological organs; biological tissues; biomedical ultrasonics; Breast Neoplasms - diagnostic imaging; breast tumor; Cancer; Cell processes; cellular biophysics; Diagnosis using ultrasonic, sonic or infrasonic waves; Dictionaries; Digital computing or data processing equipment or methods, specially adapted for specific applications; Eigenvalues; Female; gynaecology; homogeneous patch; Humans; Image data processing or generation, in general; Image Interpretation, Computer-Assisted - methods; image segmentation; Image sensors; image texture; Medical image noise; medical image processing; Medical image segmentation; Medical imaging; normalized cut; Speckle; tumours; Ultrasonics; Ultrasonography; ultrasound; Ultrasound Physics; normalized cut; breast tumor; image segmentation; homogeneous patch; ultrasound
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.4718565

Purpose: Accurately segmenting breast tumors in ultrasound (US) images is a difficult problem due to their specular nature and appearance of sonographic tumors. The current paper presents a variant of the normalized cut (NCut) algorithm based on homogeneous patches (HP-NCut) for the segmentation of ultrasonic breast tumors. Methods: A novel boundary-detection function is defined by combining texture and intensity information to find the fuzzy boundaries in US images. Subsequently, based on the precalculated boundary map, an adaptive neighborhood according to image location referred to as a homogeneous patch (HP) is proposed. HPs are guaranteed to spread within the same tissue region; thus, the statistics of primary features within the HPs is more reliable in distinguishing the different tissues and benefits subsequent segmentation. Finally, the fuzzy distribution of textons within HPs is used as final image features, and the segmentation is obtained using the NCut framework. Results: The HP-NCut algorithm was evaluated on a large dataset of 100 breast US images (50 benign and 50 malignant). The mean Hausdorff distance measure, the mean minimum Euclidean distance measure and similarity measure achieved 7.1 pixels, 1.58 pixels, and 86.67%, respectively, for benign tumors while those achieved 10.57 pixels, 1.98 pixels, and 84.41%, respectively, for malignant tumors. Conclusions: The HP-NCut algorithm provided the improvement in accuracy and robustness compared with state-of-the-art methods. A conclusion that the HP-NCut algorithm is suitable for ultrasonic tumor segmentation problems can be drawn.