A High-Speed Architecture for Lung Cancer Diagnosis

Abstract

Here the authors propose a simplified technique and its architecture for blind segmentation of histopathological images of lung cancer, combining the K-Means and Histogram analysis. An improved version of Otsu's algorithm is introduced for performing histogram analysis to determine the number of clusters for executing the automatic segmentation of histopathological images. The architecture is input with Biopsy images of cancer patients suffering from different stages of Lung cancer, procured from standard hospital databases to evaluate the performance. The results obtained are compared with the existing works from the literature showing considerable improvement in the overall efficiency of the image segmentation process. Segmentation output in terms of quantitative parameters like PSNR, SSIM, time of execution, etc., as well as qualitative analysis, clearly reveals the usefulness of this technique in high-speed cytological evaluation. The proposed architecture gives promising results in terms of its performance with a time of execution of 192.25ms.