Content Based Image Retrieval of Ultrasound Liver Diseases Based on Hybrid Approach

Abstract

Problem statement:In the past few years, immense improvement was obtained in the field of Content-Based Image Retrieval (CBIR). Nevertheless, existing systems still fail when applied to medical image databases. Simple feature-extraction algorithms that operate on the entire image for characterization of color, texture, or shape cannot be related to the descriptive semantics of medical knowledge that is extracted from images by human experts. Approach: In this study, we present a hybrid approach called Support vector machine combined with relevance feedback for the retrieval of liver diseases from Ultrasound (US) images is introduced. SVM and RF are supervised active learning technique used to improve the effectiveness of the retrieval system. Three kinds of liver diseases are identified including cyst, alcoholic cirrhosis and carcinoma. The diagnosis scheme includes four steps: image registration, feature extraction, feature selection and image retrieval. First the ultrasound images are registered in the database based on the modality. Then the features, derived from first order statistics, gray level co-occurrence matrix and fractal geometry, are obtained from the Pathology Bearing Regions (PBRs) among the normal and abnormal ultrasound images. The Correlation Based Feature Selection (CFS) algorithm selects the certain features for the specific diseases and also reduces dimensionality space for classification. Finally, we implement our hybrid approach for retrieval of specific diseases from the database. Results: This hybrid approach can get the query from user and has retrieved both positive and negative samples from the database, by getting feedback in each round from the radiologist is help to improve the retrieval of correct images. Conclusion:The hybrid approach (SVM+RF) comprises several benefits when compared to existing CBIR for medical system by neural network algorithms. Fractal geometry in feature extraction plays crucial role in ultrasound liver image retrieval. CFS also reduce the dimensionality issue during storage. Image registration plays an important role in the retrieval. It reduces the redundancy of retrieval images and increases the response rate. Getting relevance feedback from physician helps to improve the accuracy of retrieval images from the database.

Content-based Image Retrieval for Medical Application

“…JADUAL 1. Kajian yang telah dijalankan oleh ahli penyelidik Kajian Kaedah Modaliti Imej (Arakeri et al 2012) Pengelasan hierarki dan persamaan kandungan MRI (Suganya et al 2012) SVM digabungkan dengan maklumbalas relevan Ultrasound (Nakaram et al 2012) Discrete Wavelet Transform (DWT) X-Ray (Murala and Jonathan Wu 2013) Local ternary co-occurrence patterns MRI dan CT (Arthi et al 2013) CCM (Colour Co-occurrence Matrix) menggunakan peta warna Imej Perubatan Hue Saturation Value (HSV) (Grace et al 2014) Rangka Apache Hadoop Imej Perubatan Berasaskan kaedah graf menggunakan vertex set dan edge set PET-CT (Wan Ahmad et al 2014) Gabor transform, Discrete Wavelet Frame, Grey Level Histogram X-Ray dan kombinasi kaedah ini (Bergamasco and Nunes 2015) Fitur global dan tempatan: Distance Histogram Descriptor, Local Distance MRI Histogram Descriptor, dan 3D Hough Transform Descriptor (Kitanovski et al 2016) Berasaskan hasil pengkuantuman dan pengelasan SVM Imej Perubatan (Sparks and Madabhushi 2016) Out-of-Sample Extrapolation menggunakan Semi-Supervised Manifold Histologi Prostat Learning (OSE-SSL) (Nowaková et al 2017) Pengkuantuman vektor dengan fuzzy S-trees Mammogram (Spanier et al 2017) Hibrid: gabungan dengan penemuan radiologi dari laporan kes CT perubatan dan graf Radlex (Xu et al 2017 Dalam bidang perubatan, imej-imej kebiasaannya disimpan dalam format DICOM. Imej DICOM mengandungi pelbagai maklumat penting mengenai pesakit seperti identiti pesakit, jantina, modaliti imej, bahagian badan dan parameter mesin.…”

“…DSI ialah proses melayari, mencari, dan mendapatkan semula imej dari pangkalan data imej yang besar (Murthy et al 2010;Ho et al 2012) dengan menggunakan kata-kata kunci atau fitur-fitur imej tersebut (Suganya and Rajaram 2012). Teknologi DSI yang terawal adalah berdasarkan teks (Goodrum 2000;Ahmad 2008).…”

Feature Dimension Reduction for Efficient Medical Image Retrieval System Using Unified Framework

“…The first level of CBMIR is Feature extraction (Akgul et al, 2011;Suganya and Rajaram, 2012) which would extract the visual features and are designed as Feature Vector Database. Related to shape and color features, texture features have periodicity and scale results to the possessions of catching semantic features in images since it consumes a complete association of innumerable grey levels inside the images.…”

A Comparative Study of Combined Feature Selection Methods for Arabic Text Classification

“…Correlation-based Feature Selection (CFS) is one of commonly known techniques to evaluate and rank the relevance of features by measuring correlation between features and classes and between some features and others (Suganya and Rajaram, 2012).…”