Abstract:
Medical image segmentation plays a crucial role in various clinical applications, providing essential information for diagnosis, treatment planning, and patient monitoring. However, accurately segmenting anatomical structures from medical images remains a challenging task due to the complexity and variability of the human body. To address this challenge, the design and implementation of a methodology that combines machine learning techniques with statistical shape models (SSMs) have gained significant attention.
This research focuses on the development of a novel approach that leverages the strengths of machine learning algorithms and SSMs to improve the accuracy and efficiency of medical image segmentation. The proposed methodology consists of two main stages: training and testing. In the training stage, a dataset of annotated medical images is used to train a machine learning model, such as a convolutional neural network (CNN) or a random forest classifier. This model learns the complex patterns and relationships between image features and corresponding anatomical structures.
In the testing stage, the trained model is applied to segment new, unseen medical images. However, instead of relying solely on the machine learning model, the proposed approach integrates a statistical shape model. The statistical shape model captures the statistical distribution of shape variations within a population of training images. By combining the learned shape prior with the outputs of the machine learning model, more accurate and robust segmentations can be achieved.
The implementation of the proposed methodology involves preprocessing the medical images to enhance their quality, extracting relevant features, training the machine learning model, and integrating the statistical shape model. The evaluation of the approach is performed using various metrics, including Dice similarity coefficient and Hausdorff distance, to assess the accuracy and consistency of the segmentations compared to ground truth annotations.
The results demonstrate that combining machine learning techniques with statistical shape models yields improved segmentation accuracy, particularly in cases where the anatomical structures exhibit high variability or partial occlusions. The proposed methodology has the potential to enhance clinical decision-making, improve patient outcomes, and facilitate the development of computer-assisted diagnosis and treatment planning systems.
Keywords: Medical image segmentation, machine learning, statistical shape models, convolutional neural networks, random forest classifier, accuracy, evaluation, clinical applications.
DESIGN AND IMPLEMENTATION OF COMBINING MACHINE LEARNING TECHNIQUES WITH STATISTICAL SHAPE MODELS IN MEDICAL IMAGE SEGMENTATION, GET MOREÂ Â COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS
