OTSU’s Thresholding with supervised learning approach for cancer lesion detection
Sajid Hussain · IOSR Journal of VLSI and Signal processing · 2014
For Tracking interfaces and shapes which depends on the regions of pixel intensity is a challenging task in image segmentation.Many level set methods have been formulated for region based and edge based models in computer aided diagnosis systems.In order to provide accurate modeling involving numerical computations, contours, lesions and bias variance which often rely on pixel intensity variations for the region of Interest.The proposed method involves the formulation by deriving a global criterion function in terms of neighborhood pixels to represent domain field and bias variance characteristics.Gaussian impulse is used for smoothening sharp edges.Computational neural networks provide the integral part of most learning algorithms as images consists of redundant attributes of data which have redundant network connections with different input patterns of small weights form a network training process for minimizing the energy and to estimate the bias field correction for various imaging modalities.In order to have valuable diagnostic information in disease diagnosis the extracted features of PET and CT images are modeled with neural networks.The trained data sets are useful in removing artifacts and providing resultant ROI.