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A two-stage framework for DIC image denoising and Gabor based GLCM feature extraction for pre-cancer diagnosis

In this paper a novel two-stage adaptive framework for denoising of differential interference contrast (DIC) images followed by Gabor based gray-level co-occurrence matrix (GLCM) feature extraction methodology is proposed. The first stage consists of a hybrid cascade of anisotropic diffusion denoising (Perona–Malik diffusion) and unsharp masking (USM) based detail enhancement filter to remove noise from DIC images without losing significant morphological features of healthy and precancerous tissues while enlarging the image sharpness. The hybrid filter parameters are obtained by joint stochastic optimization of the image quality metrics. The estimated denoised image with the highest signal to noise ratio (SNR) from Stage I, is used for subsequent textural feature extraction. GLCM window considers neighborhood blocks with similar local statistics with well-preserved local structures between a pixel texture and its nearest neighbors. The efficacy of our denoised DIC imaging with Gabor based GLCM feature descriptors in analysis of healthy and precancerous tissues is experimentally validated for its competitive denoising performance and detail structure preservation of DIC images. The relative change in magnitude and phase information as manifested from Gabor filter coupled with GLCM based spatial statistical measures of tissues as cancer progress validates the adequacy of the proposed scheme for its early stage cancer detection ability in cervical tissues.