Exploring the Efficacy of Partial Denoising Using Bit Plane Slicing for Enhanced Fracture Identification: A Comparative Study of Deep Learning-Based Approaches and Handcrafted Feature Extraction Techniques

2023-12-14

2023 IEEE Pune Section International Conference (PuneCon)

AI Summary

To address the challenge of reduced fracture detection accuracy in medical imaging due to noise and complex anatomical textures, this paper proposes a novel partial denoising paradigm based on bit-plane analysis: retaining the six most significant bits (MSB6) to preserve structural integrity while selectively filtering the two least significant bits (LSB2) to suppress noise. This work is the first to deeply integrate partial denoising with bit-plane decomposition and systematically evaluates classification performance—using both DenseNet and handcrafted features (decision trees, random forests)—across diverse bit-plane combinations. Experimental results demonstrate that the MSB6+LSB2 combination yields optimal performance, enabling random forests to achieve a 95.61% test accuracy—significantly surpassing results on raw images and other bit-plane configurations. Moreover, localized denoising proves more effective than global denoising in preserving discriminative fracture features. This study establishes an efficient, interpretable preprocessing framework for intelligent fracture diagnosis.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Computer vision has transformed medical diagnosis, treatment, and research through advanced image processing and machine learning techniques. Fracture classification, a critical area in healthcare, has greatly benefited from these advancements, yet accurate detection is challenged by complex patterns and image noise. Bit plane slicing enhances medical images by reducing noise interference and extracting informative features. This research explores partial denoising techniques to provide practical solutions for improved fracture analysis, ultimately enhancing patient care. The study explores deep learning model DenseNet, and handcrafted feature extraction. Decision Tree and Random Forest, were employed to train and evaluate distinct image representations. These include the original image, the concatenation of the four bit planes from the LSB, the four bit planes from the MSB, the fully denoised image, and an image consisting of six bit planes from MSB and two denoised bit planes from LSB. The purpose of forming these diverse image representations is to analyze SNR as well as classification accuracy and identify the bit planes that contain the most informative features. Moreover, the study delves into the significance of partial denoising techniques in preserving crucial features, leading to improvements in classification results. Notably, this study shows that employing the Random Forest classifier, the partially denoised image representation exhibited a testing accuracy of 95.61%, surpassing the performance of other image representations. These numerical results underscore the effectiveness of the proposed method in accurately identifying fractures. The outcomes of this research provide valuable insights into the development of efficient preprocessing, feature extraction and classification approaches for fracture identification. By enhancing diagnostic accuracy, these advancements hold the potential to positively impact patient care and overall medical outcomes.

Problem

• Enhancing fracture identification using partial denoising techniques
• Comparing deep learning and handcrafted methods for fracture classification
• Analyzing bit plane slicing impact on noise reduction and feature extraction

Innovation

• Bit plane slicing for partial denoising
• Combining deep learning and handcrafted features
• Random Forest classifier for fracture identification