An Integrated System for the Texture Analysis of Prostate Ultrasound Images Based on Different Pre-processing Schemes

Wed, 01 Jan 2025 00:00:00 +0000

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.

Automated Prostate Segmentation in Ultrasound Images Based on Different Pre-processing Schemes

Wed, 01 Jan 2025 00:00:00 +0000

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. This algorithm was applied to a dataset of 647 TRUS images. All images were separated into four groups as follows: original (O), intensity normalized (N), despeckled (D), and intensity normalized and despeckled (ND). Manual segmentations of the prostate were performed by an experienced radiation oncologist and compared with automated segmentations using six different evaluation metrics. Statistical analysis showed that preprocessing enhances segmentation performance, with a median (±IQR) Dice coefficient of 94.02 (3.93)/94.84 (3.92)/94.43 (3.05)/94.22 (4.19) for the O/N/D/ND images respectively. The highest segmentation accuracy was achieved on the N images, followed by the ND images which confirm the benefits of N and ND in enhancing the final segmentation accuracy. No statistically significant differences were found between all different preprocessing schemes for all the evaluation metrics investigated. Due to the small number of patients, the generalizability of the results is limited. Nevertheless, the findings highlight the potential clinical value of preprocessing in improving segmentation performance in challenging ultrasound cases. Additional experimentation with a larger image dataset and other alternative evaluation metrics is required to validate the present results.

Huang Xiwei | MSc in Electronic Science and Technology

An Integrated System for the Texture Analysis of Prostate Ultrasound Images Based on Different Pre-processing Schemes

Automated Prostate Segmentation in Ultrasound Images Based on Different Pre-processing Schemes