Melanoma is a skin tumor with high malignancy. Cases of melanoma have recently been reported from many hospitals around the world. Since melanoma is analogous with benign nevus, discriminating between melanoma and benign nevus is difficult for nonspecialists. A screening system for melanoma and benign nevus is desired. Melanoma is mainly diagnosed by features of specific shape and color. However, texture patterns within the tumor region can be diagnostically important. The purpose of this research was to classify patterns on the tumor surface. Digital images of a tumor were classified into 3 patterns by texture analysis: homogeneous pattern; globular pattern; and reticular pattern. The tumor part in the image was specified first, and the specified tumor part was divided into sub-images. Texture features of each sub-image were then calculated. Patterns were classified by discriminant analysis based on the number of texture features. As a result, patterns could be classified correctly into the three categories at a ratio of 94%.
|Number of pages||8|
|Journal||IEEJ Transactions on Electrical and Electronic Engineering|
|Publication status||Published - 2008 Jan|
- Pattern classification
- Texture analysis
ASJC Scopus subject areas
- Electrical and Electronic Engineering