An Efficient Content-Based Image Retrieval System for the Diagnosis of Lung Diseases
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ORIGINAL PAPER
An Efficient Content-Based Image Retrieval System for the Diagnosis of Lung Diseases Muhammad Kashif1 · Gulistan Raja1 · Furqan Shaukat1
© Society for Imaging Informatics in Medicine 2020
Abstract The main problem in content-based image retrieval (CBIR) systems is the semantic gap which needs to be reduced for efficient retrieval. The common imaging signs (CISs) which appear in the patient’s lung CT scan play a significant role in the identification of cancerous lung nodules and many other lung diseases. In this paper, we propose a new combination of descriptors for the effective retrieval of these imaging signs. First, we construct a feature database by combining local ternary pattern (LTP), local phase quantization (LPQ), and discrete wavelet transform. Next, joint mutual information (JMI)– based feature selection is deployed to reduce the redundancy and to select an optimal feature set for CISs retrieval. To this end, similarity measurement is performed by combining visual and semantic information in equal proportion to construct a balanced graph and the shortest path is computed for learning contextual similarity to obtain final similarity between each query and database image. The proposed system is evaluated on a publicly available database of lung CT imaging signs (LISS), and results are retrieved based on visual feature similarity comparison and graph-based similarity comparison. The proposed system achieves a mean average precision (MAP) of 60% and 0.48 AUC of precision-recall (P-R) graph using only visual features similarity comparison. These results further improve on graph-based similarity measure with a MAP of 70% and 0.58 AUC which shows the superiority of our proposed scheme. Keywords CIS · CBIR · LISS · Semantic similarity · Visual similarity
Introduction In medical field, different imaging modalities like Xray, computed tomography (CT), and magnetic resonance imaging (MRI) are being used to produce numerous images which can help in diagnosis and therapy. For example, by analyzing CT scan of lungs, radiologist can take the decision whether the lung tissue or lesion is normal or abnormal. This decision is normally based on some common imaging signs (CISs). These signs (CISs) can be categorized into different types without correlating it to some particular Gulistan Raja
[email protected] Muhammad Kashif [email protected] Furqan Shaukat [email protected] 1
Faculty of Electronics and Electrical Engineering, Univeristy of Engineering & Technology, Taxila, Pakistan
disease because the same signs can appear in different diseases [1]. To date, almost 50 categories of CISs have been observed and reported by medical experts. Among them, nine categories which appear most frequently in lung CT images are air bronchogram (AB), bronchial mucus plugs (BMP), calcification, cavity and vacuolus (CV), groundglass opacity (GGO), lobulation, obstructive pneumonia (OP), pleural dragging (PI), and spiculation [2]. The sample images in which these signs appear are s
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