Detection of Structure Characteristics and Its Discontinuity Based Field Programmable Gate Array Processor in Cancer Cell by Wavelet Transform

Abstract

Digital clinical histopathology is one of the crucial techniques for precise cancer cell diagnosing in modern medicine. The Synovial Sarcoma (SS) cancer cell patterns seem to be a spindle shaped cell (SSC) structure and it is very difficult to identify the exact oval shaped cell structure through pathologist’s eye perception. Meanwhile, there is necessitating for monitoring and securing the successful and effective image data processing in the the huge network data which is also a complex one. A field programmable Gate Array (FPGA) was regarded as a necessary one for this. In this work, based on FPGA a Cancer Cell classification is made for the regulation and execution. Hence, mathematically the SSC regularity structures and its discontinuities are measured by the holder exponent (HE) function. In this research work, HE values have been determined by Wavelet Transform Modulus Maxima (WTMM) and Wavelet Leader (WL) methods with basis function of Haar wavelet based on FPGA Processor. The quantitative parameters such as Mean of Asymptotic Discontinuity (MAD), Mean of Removable Discontinuity (MRD) and Number of Discontinuity Points (NDPs) have been considered to determine the prediction of discontinuity detection between WTMM and WL methods. With the help of receiver operating characteristics (ROC) curve, the significant difference of discontinuity detection performance between both the methods has been analyzed. From the experimental results, it is clear that the WL method is more practically feasible and it gives satisfactory performance, in terms of sensitivity and specificity percentage values, which are 80.56% and 59.46%, respectively in the blue color components of the SNR 20 dB noise image.