A Noninvasive Cancer Detection Using Hyperspectral Images

The cancer remained as one of the major cause of mortality worldwide. Instead of traditional excitional biopsy, it will be ideal to put forward the cancer detection using invasive techniques, it can make the treatment more effective and simple. Here a self sufficient classification method has been introduced that combines both spectral and spatial information on hyperspectral images of an animal tissue in distinguishing cancerous via healthy. An automated algorithm based on an optimal band selection and Minimum Spanning Forest (MSF) algorithm has been proposed for classification of hyperspectral images. The segmentation has been achieved by collecting the features from the selected markers in the optimal band. In optimal band selection, the unnecessary band may get eliminated and selects a range where there is a probability of having abnormality and the markers are being selected using the intensity feature available in the selected band. From these numerous markers available, the problematic region if available, may be differentiated using the MSF algorithm. The MSF is utilizing the pixel distribution of every marker. The SVM classifier is finally used to distinguish between the normal or abnormal based on the MSF features. The corresponding MSF based advanced cancer detection scheme is accurate and efficient, making it as a good noninvasive tool. It is not only applicable in early cancer detection, but also as an interpretive tool in tumor margin resection.