scholarly journals Content based Retrieval of Liver Images for Computer Aided Diagnosis

2019 ◽  
Vol 8 (6) ◽  
pp. 555-562
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Region Growing ◽  
Tumor Segmentation ◽  
Distance Metric ◽  
Medical Database ◽  
Query Image ◽  
Serious Disease ◽  
Occurrence Matrix ◽  
Metric Distances

Liver cancer is a serious disease caused by a variety of factors that damage the liver region. Early detection of this disease is necessary to diagnose and to cure it completely. Enormous increase in medical database images has lead to development of Content Based Image Retrieval (CBIR) system to retrieve relevant liver images from medical database consisting of abdominal Computed Tomography (CT) images. In the proposed method Content Based Medical Image Retrieval (CBMIR) system is designed to search and retrieve relevant liver images from medical image database. Adaptive Region Growing Algorithm (ARGA) and Simple Linear Iterative Clustering (SLIC) are used for liver and tumor segmentation. Features are extracted using Gray Level Co-occurrence Matrix (GLCM), Average Correction High order Local Autocorrelation Coefficients (ACHLAC) and Legendre Moments (LM). Based on the distance metric, distances between extracted features of query image and images in the database are measured. Euclidean distance metric is used to retrieve relevant medical images.

scholarly journals Effective Diagnosis and Treatment through Content-Based Medical Image Retrieval (CBMIR) by Using Artificial Intelligence

2019 ◽  
Vol 8 (4) ◽  
pp. 462 ◽  
Author(s):  
Muhammad Owais ◽  
Muhammad Arsalan ◽  
Jiho Choi ◽  
Kang Ryoung Park
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Image Retrieval ◽  
Medical Image ◽  
Medical Images ◽  
Diagnosis And Treatment ◽  
Imaging Modalities ◽  
Imaging Data ◽  
Medical Database ◽  
Medical Image Retrieval

Medical-image-based diagnosis is a tedious task‚ and small lesions in various medical images can be overlooked by medical experts due to the limited attention span of the human visual system, which can adversely affect medical treatment. However, this problem can be resolved by exploring similar cases in the previous medical database through an efficient content-based medical image retrieval (CBMIR) system. In the past few years, heterogeneous medical imaging databases have been growing rapidly with the advent of different types of medical imaging modalities. Recently, a medical doctor usually refers to various types of imaging modalities all together such as computed tomography (CT), magnetic resonance imaging (MRI), X-ray, and ultrasound, etc of various organs in order for the diagnosis and treatment of specific disease. Accurate classification and retrieval of multimodal medical imaging data is the key challenge for the CBMIR system. Most previous attempts use handcrafted features for medical image classification and retrieval, which show low performance for a massive collection of multimodal databases. Although there are a few previous studies on the use of deep features for classification, the number of classes is very small. To solve this problem, we propose the classification-based retrieval system of the multimodal medical images from various types of imaging modalities by using the technique of artificial intelligence, named as an enhanced residual network (ResNet). Experimental results with 12 databases including 50 classes demonstrate that the accuracy and F1.score by our method are respectively 81.51% and 82.42% which are higher than those by the previous method of CBMIR (the accuracy of 69.71% and F1.score of 69.63%).

scholarly journals An Novel Framework For Content Based Image Retrieval With Quality Assessment System using Optimal Deep Convolution Neural Network

2019 ◽  
Vol 8 (12) ◽  
pp. 3916-3926
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Feature Vector ◽  
Structural Similarity ◽  
Extraction Process ◽  
Input Image ◽  
Assessment System ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Occurrence Matrix

Content based image retrieval (CBIR) models become popular for retrieving images connected to the query image (QI) from massive dataset. Feature extraction process in CBIR plays a vital role as it affects the system’s performance. This paper is focused on the design of deep learning (DL) model for feature extraction based CBIR model. The presented model utilizes a ResNet50 with co-occurrence matrix (RCM) model for CBIR. Here, the ResNet50 model is applied for feature extraction of the QI. Then, the extracted features are placed in the feature repository as a feature vector. The RCM model computes the feature vector for every input image and compares it with the features present in the repository. Then, the images with maximum resemblance will be retrieved from the dataset. In addition, the resemblance between the feature vectors is determined by the use of co-occurrence matrix subtraction process. Besides, structural similarity (SSIM) measure is applied for the validation of the similarity among the images. A comprehensive results analysis takes place by the use of Corel 10K dataset. The experimental outcome indicated the superiority of the RCM model with respect to precision, recall and SSIM.

scholarly journals A New Optimized Hybrid Local Lifting Wavelet Co-occurrence Texture Pattern for Content Based Medical Image Retrieval

2021 ◽  
Vol 17 (11) ◽  
pp. 157
Author(s):  
R Varaprasada Rao ◽  
T Jaya Chandra Prasad
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Optimization Technique ◽  
Histogram Equalization ◽  
Directional Pattern ◽  
Query Image ◽  
Medical Image Retrieval ◽  
Texture Pattern ◽  
Feature Descriptors ◽  
Lifting Wavelet

<p class="0abstract">Medical image retrieval (MIR) is a hard task owing to the varied patterns and structures in the medical images. The feature descriptors have been used to describe the images in most MIR approaches. Based on the local relationship, several feature descriptors of neighbouring image pixels have been proposed for MIR so far, but their low performance scores make them unsuitable. In this paper, an efficient optimized hybrid local lifting wavelet co-occurrence texture pattern for content-based MIR is proposed. Initially, image resize and Adaptive histogram equalization technique is used to carried out for contrast enhancement. Then Local Lifting Wavelet Co-occurrence Texture Pattern is derived using Local tetra pattern, Gradient directional pattern, lifting wavelet transform and Gray level co-occurrence matrix. An Equilibrium optimization technique is employed to select the most important features of an image from the obtained feature vectors (FV). Finally, to match the query image with the database images, distance between their FV is computed and the minimum distance images are considered as retrieval outcome. Three benchmark medical databases of various modalities (CT and MRI) are used to test the efficiency of the proposed method: EXACT-09, TCIA-CT, and OASIS. The experimental results prove that the proposed approach outperforms existing descriptors in terms of APR and ARR.</p>

scholarly journals Improved Content Based Medical Image Retrieval using PCA with SURF Features

2019 ◽  
Vol 8 (10S) ◽  
pp. 112-114
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Feature Vector ◽  
Principal Component ◽  
Medical Field ◽  
Huge Number ◽  
Query Image ◽  
Medical Image Retrieval ◽  
Image Retrieval System ◽  
Surf Features

In the computer era, the Content Based Image Retrieval system (CBIR) has most widely used in medical field and crime invention. During the last decade, CBIR emerged as powerful tool to efficiently retrieved images visually similar to query image. The basic process behind this concept is representation of image as feature vector and to measure the similarities between the images with distance between their corresponding feature vectors according to some metrics. The finding of correct features to represent images with, as well as the similarity metric that groups visually similar image together, are important milestone in construction of any CBIR system .The work in this paper focused on retrieve the correct query image from a huge number of medical image databases with the help of Principal Component Analysis (PCA) through SURF feature vector detection. The combination of this method produces an accurate and quick response than other conventional methods like SIFT and SURF feature vector based medical image retrieval.

scholarly journals Content based Image Retrieval using Statistical Parameters of a Medical Image

2019 ◽  
Vol 8 (3) ◽  
pp. 3649-3653
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Medical Images ◽  
Content Based Image Retrieval ◽  
Statistical Parameters ◽  
Query Image ◽  
Similar Images ◽  
Matching Score ◽  
The Cost ◽  
The Given

We present a framework that permits in classifying medical images so as to recognize conceivable diseases that affected. This is done by Image retrieval from the collection of dataset by inputting the query image. Content based Image retrieval (CBIR) is the way toward seeking comparable pictures from a picture database dependent on the visual substance of the given query image. Even though some studies present general method in image extraction, there are no efficient methods in medical image retrieval with accuracy. To overcome and to eliminate these flaws our proposed CBIR method examined with the accurate and efficient way for feature extraction from medical images. The images used are grey scale image. The dataset holds the n number of images related to medical particularly brain tumor images. To retrieve the related images from the dataset and get the corresponding details, image is given as an input i.e., query image. Initially, the query image is analyzed by shape, texture and histogram and the result obtained from this is compared with the similar images in dataset. The similarities between the images are found by implementing the Matching Score algorithm. This algorithm provides accuracy in matching the image that helps greatly at the time of classification. The results of computation is said to be the features for the given image. Also the cost for processing the image is comparatively low. The technique has been examined on standard image dataset and satisfactory results have been achieved

Extract the Similar Images Using the Grey Level Co-Occurrence Matrix and the Hu Invariants Moments

2020 ◽  
Vol 38 (5A) ◽  
pp. 719-727
Author(s):  
Beshaier A. Abdulla ◽  
Yossra H. Ali ◽  
Nuha J. Ibrahim
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Distance Measure ◽  
Similarity Index ◽  
Structural Similarity ◽  
Grey Level ◽  
Query Image ◽  
Image Retrieval System ◽  
Similar Images ◽  
Occurrence Matrix

In the last years, many types of research have introduced different methods and techniques for a correct and reliable image retrieval system. The goal of this paper is a comparison study between two different methods which are the Grey level co-occurrence matrix and the Hu invariants moments, and this study is done by building up an image retrieval system employing each method separately and comparing between the results. The Euclidian distance measure is used to compute the similarity between the query image and database images. Both systems are evaluated according to the measures that are used in detection, description, and matching fields which are precision, recall, and accuracy, and addition to that mean square error (MSE) and structural similarity index (SSIM) is used.  And as it shows from the results the Grey level co-occurrence matrix (GLCM) had outstanding and better results from the Hu invariants moment method.

Design and Implementation of Content-Based Natural Image Retrieval Approach Using Feature Distance

2020 ◽  
Vol 20 (02) ◽  
pp. 2050014
Author(s):  
S. L. Arunlal ◽  
N. Santhi ◽  
K. Ramar
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Back Propagation ◽  
Texture Feature ◽  
Input Image ◽  
Geographical Information ◽  
Natural Image ◽  
Medical Database ◽  
Query Image ◽  
Artificial Neural Network Ann

Generally, the database is a gathering of data that is arranged for simple storage, retrieval and modernize. This data comprises of numerous structures like text, table, and image, outline and chart and so on. Content-based image retrieval (CBIR) is valuable for calculating the huge amount of image databases and records and for distinguishes retrieving similar images. Rather than text-based searching, CBIR effectively recovers images that are similar like query image. CBIR assumes a significant role in various areas including restorative finding, industry estimation, geographical information satellite frameworks (GIS frameworks), and biometrics; online searching and authentic research, etc. Here different medical database images are considered to the CBIR procedure is done by the proposed strategy. The proposed method considers the input features are shape, texture feature, wavelet feature, and SIFT feature. To retrieve the input image based on the features, the suggested method utilizes artificial neural network (ANN) structure. Back-propagation technique, which is an organizational structure for learning is utilized for training the neural network framework. Trial demonstrates that the proposed work improves the results of the retrieval system. From the outcomes minimizes the image retrieval time and maximum Precision 87.3% in distance based ANN process.

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