12/16/2023 0 Comments Absolute brightnessImage enhancement plays an important role in image processing to obtain an image with more perceptual details. The average edge-based contrast measure (EBCM) of 83.065 demonstrates the best contrast improvement of the proposed methods compared with the other state-of-the-art methods. Noises are minimized, while the contrast and colour of phytoplankton cells are improved. ![]() Results demonstrated that DIHS-BR filtered out the image background and left only the required phytoplankton cell image. DIHS-BR consists of two major steps, namely, contrast adaptive histogram specification and background removal by means of edge mask cropping. DIHS-BR will automatically remove the background and noises. Integrated dual image contrast adaptive histogram specification with enhanced background removal (DIHS-BR) is proposed to address these issues by automatically removes the background of the phytoplankton image and improves the image quality while cropping phytoplankton cell. These images are not appropriated for identification. The captured phytoplankton microscopic images suffer from low contrast and surrounding debris. The integration of a feature driven methodology to explain the predictions after applying image enhancement methods, enables the development of a concrete, trustworthy automated pipeline for prostate segmentation on MR images.ĭiatom is a dominant phytoplankton and commonly found in oceans or waterways. Overall, RACLAHE was the most consistent image enhancement algorithm in terms of performance improvement across CNN models with the mean increase in Dice Score ranging from 3 to 9% for the different prostatic regions, while achieving minimal inter-model variability. ![]() Additionally, a methodology is proposed to explain, both quantitatively and qualitatively, the relation between saliency maps and ground truth probability maps. The improvement in performance and consistency across five CNN models (U-Net, U-Net++, U-Net3+, ResU-net and USE-NET) is compared against four popular image enhancement methods. The present work introduces a novel image enhancement method, named RACLAHE, to enhance the performance of CNN models for segmenting the prostate’s gland and the prostatic zones. Particularly, for prostate segmentation, the effectiveness of image enhancement on different Convolutional Neural Networks (CNN) remains largely unexplored. ![]() While different image enhancement techniques are emerging as powerful tools for improving the performance of segmentation algorithms, their application still lacks consensus due to contrasting evidence regarding performance improvement and cross-model stability, further hampered by the inability to explain models’ predictions. Automatic segmentation of the prostate of and the prostatic zones on MRI remains one of the most compelling research areas.
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