A MEDICAL X-RAY IMAGE CLASSIFICATION AND RETRIEVAL SYSTEM

Medical image retrieval systems have gained high interest in the scientific community due to the advances in medical imaging technologies. The semantic gap is one of the biggest challenges in retrieval from large medical databases. This paper presents a retrieval system that aims at addressing this challenge by learning the main concept of every image in the medical database. The proposed system contains two modules: a classification/annotation and a retrieval module. The first module aims at classifying and subsequently annotating all medical images automatically. SIFT (Scale Invariant Feature Transform) and LBP (Local Binary Patterns) are two descriptors used in this process. Image-based and patch-based features are used as approaches to build a bag of words (BoW) using these descriptors. The impact on the classification performance is also evaluated. The results show that the classification accuracy obtained incorporating image-based integration techniques is higher than the accuracy obtained by other techniques. The retrieval module enables the search based on text, visual and multimodal queries. The text-based query supports retrieval of medical images based on categories, as it is carried out via the category that the images were annotated with, within the classification module. The multimodal query applies a late fusion technique on the retrieval results obtained from text-based and image-based queries. This fusion is used to enhance the retrieval performance by incorporating the advantages of both text-based and content-based image retrieval

Feature Extraction Techniques in Medical Imaging: A Systematic Review

With the surge in the development of various applications in the field of Computer Vision and Digital Image Processing, a significant amount of medical pictures are being produced. Thus, the patient-specific scan pictures represent the boundless volume of data that requires careful organization and supervision to assist clinical decision support systems. Now that retrieval, classification, segmentation, and other procedures have been completed, these devices assist doctors to uncover serious illnesses including skin conditions, tumors, and cancer. This imaging largely depends on characteristics to detect the afflicted region and perform the diagnosis visually. The authors of this paper present an overview of numerous feature extraction approaches used to extract features from medical images obtained via different modalities, but only used a handful of these techniques for this job and provided the findings

Computer-aided diagnosis through medical image retrieval in radiology.

Currently, radiologists face an excessive workload, which leads to high levels of fatigue, and consequently, to undesired diagnosis mistakes. Decision support systems can be used to prioritize and help radiologists making quicker decisions. In this sense, medical content-based image retrieval systems can be of extreme utility by providing well-curated similar examples. Nonetheless, most medical content-based image retrieval systems work by finding the most similar image, which is not equivalent to finding the most similar image in terms of disease and its severity. Here, we propose an interpretability-driven and an attention-driven medical image retrieval system. We conducted experiments in a large and publicly available dataset of chest radiographs with structured labels derived from free-text radiology reports (MIMIC-CXR-JPG). We evaluated the methods on two common conditions: pleural effusion and (potential) pneumonia. As ground-truth to perform the evaluation, query/test and catalogue images were classified and ordered by an experienced board-certified radiologist. For a profound and complete evaluation, additional radiologists also provided their rankings, which allowed us to infer inter-rater variability, and yield qualitative performance levels. Based on our ground-truth ranking, we also quantitatively evaluated the proposed approaches by computing the normalized Discounted Cumulative Gain (nDCG). We found that the Interpretability-guided approach outperforms the other state-of-the-art approaches and shows the best agreement with the most experienced radiologist. Furthermore, its performance lies within the observed inter-rater variability

Classification of Medical Data Based On Sparse Representation Using Dictionary Learning

Due to the increase in the sources of image acquisition and storage capacity, the search for relevant information in large medical image databases has become more challenging. Classification of medical data into different categories is an important task, and enables efficient cataloging and retrieval with large image collections. The medical image classification systems available today classify medical images based on modality, body part, disease or orientation. Recent work in this direction seek to use the semantics of medical data to achieve better classification. However, representation of semantics is a challenging task and sparse representation has been explored in this thesis for this task

Content based medical image retrieval using dictionary learning

n this paper, a clustering method using dictionary learning is proposed to group large medical databases. An approach grouping similar images into clusters that are sparsely represented by the dictionaries and learning dictionaries simultaneously via K-SVD is proposed. A query image is matched with the existing dictionaries to identify the dictionary with the sparsest representation using an Orthogonal Matching Pursuit (OMP) algorithm. Then images in the cluster associated with this dictionary are compared using a similarity measure to retrieve images similar to the query image. The main features of the method are that it requires no training data and works well on the medical databases which are not restricted to specific context. The performance of the proposed method is examined on IRMA test image database. The experimental results demonstrate the efficacy of the proposed method in the retrieval of medical images