Segmentation of images of skin lesions using color and texture information of surface pigmentation.
Image segmentation algorithms extract regions on the basis of similarity of a predefined image feature such as gray-level value. In many applications, images that exhibit a variety of structure or texture cannot be adequately segmented by gray-level values alone. Additional features related to the structure of the image are needed to segment such images. Images of skin lesions exhibit significant variations in color hues as well as geometrical appearance of local surface structure. For example, images of cutaneous malignant melanoma exhibit a rich combination of color and geometrical structure of pigmentation. In these images, the local repetition of the geometrical surface structure provides the basis for the appearance of a texture pattern in the neighborhood region. For obtaining meaningful segmentation of images of skin lesions, a multichannel segmentation algorithm is proposed in this paper which uses both gray-level intensity and texture-based features for region extraction. The intensity-based segmentation is obtained using the modified pyramid-based region extraction algorithm. The texture-based segmentation is obtained by a bilevel shifted-window processing algorithm that uses new generalized co-occurrence matrices. The results of individual segmentations obtained from different channels, representing the complete set of color and texture information, are analyzed using heuristic merging rules to obtain the final color- and texture-based segmentation. Simulated as well as real images of skin lesions, representing various color shades and textures, have been processed. We show that using contrast link information in the pyramid-based region extraction process, and using the absolute magnitude and directional information in the generalized co-occurrence matrices (GCM) method, significant improvement in image segmentation can be obtained. Further, by incorporating the merging rules better results are obtained than those obtained using the gray-level intensity feature alone.
Dhawan
A P
AP
Department of Electrical and Computer Engineering, University of Cincinnati, OH 45221.
Sicsu
A
A
D000465:Algorithms; D003116:Color; D003936:Diagnosis, Computer-Assisted; D005103:Expert Systems; D005583:Fourier Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008545:Melanoma / Q000175:diagnosis*; D010363:Pattern Recognition, Automated; D012878:Skin Neoplasms / Q000175:diagnosis*; D013499:Surface Properties