Advanced Image Content Analysis Techniques Applied to Medical Imaging

Abstract

Artificial Inteligence (AI) techniques like Machine Learning (ML) and Deep Learning (DL) have the potential to revolutionize healthcare by helping doctors and healthcare professionals to analyze and diagnose patient data and images more accurately and efficiently. The integration of methods and techniques that support more effective clinical diagnosis based on images obtained from various imaging modalities, which have become increasingly widely and successfully used to detect illnesses, is necessary for a correct diagnosis, which necessitates the precise identification of each disease. Texture analysis involves the use of computer algorithms to analyze the spatial distribution of gray levels in an image of a bone sample. This analysis can provide information about the size, shape, and orientation of the bone’s microstructure, which can be used to estimate the degree of osteoporosis. Despite the substantial correlation between sick and healthy bones, the limited results of the majority of this context’s research result from the use of handcrafted methods to directly extract bone image features. To bring performance closer to clinical diagnosis, novel learning, optimization, and inference methods for processing biomedical and healthcare data must be investigated. In this work, a set of descriptors derived from a thorough study of bone texture images will be applied to a handcrafted feature extraction method (such as HOG and/or LPQ) using Gabor’s filter bank. In addition, the classifier uses bat-inspired algorithm-based optimization to automatically adjust the Gabor filter settings in order to achieve deep analysis behavior and optimal performance. With a good performance of 89.66 % for Osteoporosis diagnosis, our experimental results using a standard osteoporosis database demonstrate a significant improvement over current Deep/Handcrafted methods. Deep Feature Analysis enables the model to automate the extraction of relevant features and complete the necessary classification. By automating the feature extraction process, deep feature analysis can significantly improve the accuracy and efficiency of classification tasks.