| 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 investigates the impact of various preprocessing techniques, including ultrasound image normalization (N), despeckle filtering (D), and normalization and despeckle filtering (ND) on texture features. We seek to improve the diagnostic precision of PCa by using the variability in texture features taken from the prostate. Image normalization and despeckling methods were employed, where image quality was evaluated using four different evaluation metrics (EM) and a large number of texture features extracted from the automated segmented prostate area. Statistical analyses were used to assess the stability and diagnostic reliability of texture features extracted under different preprocessing schemes. A number of features demonstrated robustness, whereas others exhibited larger variability. This study confirmed the advantages of N, D and ND in improving the image quality and stability of features in PCa ultrasound images. Additional experimentation with a larger image dataset and other alternative evaluation metrics is required to validate the present results. |
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