image pre-processing

Prostate cancer (PCa) is a major global health concern for men and the ability to detect it in its early stages is important. While imaging modalities such as Transrectal Ultrasound (TRUS) constitutes a critical role in diagnosis, challenges such as noise and limited specificity hinder their effectiveness especially when features are extracted from the images which may be used for classification of cancer.

This study proposes an automated segmentation of prostate cancer in transrectal ultrasound images using different preprocessing methods to enhance the segmentation accuracy. We propose the use of image intensity normalization and despeckle filtering, individually and in combination, as preprocessing techniques to improve the performance of a deep learning segmentation model (DeepLabv3 +) in ultrasound images of prostate cancer.