Guided filter based image enhancement for focal error compensation in low cost automated histopathology microscopic system

Abstract

Low-cost automated histopathology microscopy systems usually suffer from optical imperfections, producing images that are slightly Out of Focus (OoF). In this work, a guided filter (GF) based image preprocessing is proposed for compensating focal errors and its efficacy is demonstrated on images of healthy and malaria infected red blood cells (h-RBCs and i-RBCs), and PAP smears. Since contrast enhancement has been widely used as an image preprocessing step for the analysis of histopathology images, a systematic comparison is made with six such prominently used methods, namely Contrast Limited Adaptive Histogram Equalization (CLAHE), RIQMC-based optimal histogram matching (ROHIM), modified L 0 , Morphological Varying(MV)-Bitonic filter, unsharp mask filter and joint bilateral filter. The images enhanced using GF approach lead to better segmentation accuracy (upto 50% improvement over native images) and visual quality compared to other approaches, without any change in the color tones. Thus, the proposed GF approach is a viable solution for rectifying the OoF microscopy images without the loss of the valuable diagnostic information presented by the color tone.

Pebrine diagnosis using quantitative phase imaging and machine learning

“…The focused and defocused images of the spores required to solve TIE for generating QPI are captured (in various z planes) using an assembled general purpose brightfield microscope, 36–38 shown in Figure 2 and its schematic diagram in Figure 3. The white LED (3 W), aperture and aspheric condenser lens are used to illuminate the sample plane uniformly.…”

Combination of Image Enhancement and U-Net Architecture for Cervical Cell Semantic Segmentation

“…CLAHE has the advantage of being able to suppress noise in the same area and increase the contrast and edges of objects (W. Li et al, 2019). In addition to having advantages, the disadvantage of using CLAHE contrast enhancement is that there is image distortion due to excessive enhancement so important features of the image cannot be extracted properly (Awasthi et al, 2020). The disadvantages of CLAHE can be overcome by the Adaptive Gamma Correction method.…”