scholarly journals Bio-medical Image Retrieval using Various Statistical Methods

2019 ◽  
Vol 9 (2) ◽  
pp. 2669-2676
Keyword(s):  
Image Retrieval ◽  
Statistical Methods ◽  
Complex Disease ◽  
Textural Features ◽  
Query Image ◽  
Imaging Technologies ◽  
Disease Patterns ◽  
Combined Features ◽  
Detection And Diagnosis ◽  
Occurrence Matrix

In recent, the healthcare sectors rely more on imaging technologies for early detection and diagnosis of the disease. But, the abundant images obtained from these imaging technologies have complex disease patterns associated with them and thus an expert requires more time to analyze and arrive at the decision. Hence, the image retrieval techniques have a significant role to assist the experts by retrieving the most similar images existing in the database and also help them to compare a new scan of the patient with the top matched images and arrive at the quick decision during the diagnosis of a patient. So, we have performed our studies on the two-dimensional structural Magnetic Resonance Imaging of the Open Access Series of Imaging Studies dataset. The collected images are preprocessed and categorized into different groups based on the ventricular region of the brain. After the categorization, we employ second and higher-order statistical approaches to extract the textural features. Then the computed textural features of the images existing in the dataset are compared with the textural features of a query image to retrieve the top matched images using similarity distance as the metric. Then the image retrieval performances of the proposed hybrid based statistical methods are measured. The obtained results shows that the combined features of Gray Level Co-occurrence Matrix and Law’s Texture Energy Measure attains the highest precision across the categorized groups of a dataset and it achieves 80% precision for Group1, Group2 images and 60% precision for Group3 images.

An Image Retrieval Algorithm Base on Texture Features

2014 ◽  
Vol 668-669 ◽  
pp. 1041-1044
Author(s):  
Lin Lin Song ◽  
Qing Hu Wang ◽  
Zhi Li Pei
Keyword(s):  
Image Retrieval ◽  
Statistical Methods ◽  
Texture Features ◽  
Retrieval Algorithm ◽  
Occurrence Matrix ◽  
Algorithm Base

This paper firstly studies the texture features. We construct a gray-difference primitive co-occurrence matrix to extract texture features by combining statistical methods with structural ones. The experiment results show that the features of the gray-difference primitive co-occurrence matrix are more delicate than the traditional gray co-occurrence matrix.

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.

A FUZZY FRAMEWORK FOR CONTENT BASED MAGNETIC RESONANCE IMAGES RETRIEVAL USING SALIENCY MAP

2017 ◽  
Vol 29 (05) ◽  
pp. 1750033
Author(s):  
Mana Tarjoman
Keyword(s):  
Magnetic Resonance ◽  
Image Retrieval ◽  
Fuzzy Inference ◽  
Saliency Map ◽  
Magnetic Resonance Images ◽  
Image Features ◽  
Research Field ◽  
Textural Features ◽  
Query Image ◽  
Inference System

Content-based image retrieval (CBIR) has turned into an important research field with the advancement in multimedia and imaging technology. The term CBIR has been widely used to describe the process of retrieving desired images from a large collection on the basis of features such as color, texture and shape that can be automatically extracted from the images themselves. Considering the gap between low-level image features and the high-level semantic concepts in the CBIR, we proposed an image retrieval system for brain magnetic resonance images based on saliency map. First, the proposed approach exploits the ant colony optimization (ACO) technique to measure the image’s saliency through ants’ movements on the image. The textural features are then calculated from the saliency map of the images. The image retrieval of the proposed CBIR system is based on textural features and the fuzzy approach using Adaptive neuro-fuzzy inference system (ANFIS). Regarding the various categories of images in a database, we define some membership functions in the ANFIS output in order to determine the membership values of the images related to the existing categories. In online image retrieval, a query image is introduced to the system and the relevant images can be retrieved based on query membership values into different classes including normal or tumoral. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency compared with the previous works.

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.

scholarly journals A new approach towards Image retrieval using texture statistical methods

2018 ◽  
Vol 2 (1) ◽  
pp. 13
Author(s):  
R Tamilkodi ◽  
G. Rosline Nesa Kumari ◽  
S. Maruthu Perumal
Keyword(s):  
Image Retrieval ◽  
Statistical Methods ◽  
Image Database ◽  
New Method ◽  
Query Image ◽  
New Approach ◽  
Energy Entropy ◽  
Retrieval Result ◽  
Canberra Distance ◽  
Texture Characterization

Texture is a possession that represents the facade and arrangement of an image. Image textures are intricate ocular patterns serene of entities or regions with sub-patterns with the kind of brightness, color, outline, dimension, and etc.This article proposes a new method for texture characterization by using statistical methods (TCUSM). In this proposed method (TCUSM) the features are obtained from energy, entropy, contrast and homogeneity. In an image, each one pixel is enclosed by 8 nearest pixels. The confined in turn for a pixel can be extracted from a neighbourhood of 3x3 pixels, which represents the fewest absolute unit. We used four vector angles 0, 45, 90,135 to carry out the experimentation with the query image. A total of 16 texture values are calculated per unit. Compute the feature vectors for the query image by calculating texture unit and the resultant value is compared with the image database. The retrieval result shows that the performance using Canberra distance has achieved higher performance. 

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 A Phantom Study to Investigate Robustness and Reproducibility of Grey Level Co-Occurrence Matrix (GLCM)-Based Radiomics Features for PET

2021 ◽  
Vol 11 (2) ◽  
pp. 535
Author(s):  
Mahbubunnabi Tamal
Keyword(s):  
Textural Features ◽  
Strongly Correlated ◽  
Segmentation Method ◽  
Diagnosis And Prognosis ◽  
Radiotracer Uptake ◽  
Positron Emission ◽  
Occurrence Matrix ◽  
The Impact ◽  
Robust To Noise

Quantification and classification of heterogeneous radiotracer uptake in Positron Emission Tomography (PET) using textural features (termed as radiomics) and artificial intelligence (AI) has the potential to be used as a biomarker of diagnosis and prognosis. However, textural features have been predicted to be strongly correlated with volume, segmentation and quantization, while the impact of image contrast and noise has not been assessed systematically. Further continuous investigations are required to update the existing standardization initiatives. This study aimed to investigate the relationships between textural features and these factors with 18F filled torso NEMA phantom to yield different contrasts and reconstructed with different durations to represent varying levels of noise. The phantom was also scanned with heterogeneous spherical inserts fabricated with 3D printing technology. All spheres were delineated using: (1) the exact boundaries based on their known diameters; (2) 40% fixed; and (3) adaptive threshold. Six textural features were derived from the gray level co-occurrence matrix (GLCM) using different quantization levels. The results indicate that homogeneity and dissimilarity are the most suitable for measuring PET tumor heterogeneity with quantization 64 provided that the segmentation method is robust to noise and contrast variations. To use these textural features as prognostic biomarkers, changes in textural features between baseline and treatment scans should always be reported along with the changes in volumes.

Enhanced Content-Based Image Retrieval Using Information Oriented Angle-Based Local Tri-Directional Weber Patterns

2021 ◽  
pp. 2150046
Author(s):  
Gangavarapu Venkata Satya Kumar ◽  
Pillutla Gopala Krishna Mohan
Keyword(s):  
Image Retrieval ◽  
Computer Applications ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
The Novel ◽  
Image Content ◽  
Query Image ◽  
Proposed Model ◽  
Maximum Mutual Information ◽  
Basic Features

In diverse computer applications, the analysis of image content plays a key role. This image content might be either textual (like text appearing in the images) or visual (like shape, color, texture). These two image contents consist of image’s basic features and therefore turn out to be as the major advantage for any of the implementation. Many of the art models are based on the visual search or annotated text for Content-Based Image Retrieval (CBIR) models. There is more demand toward multitasking, a new method needs to be introduced with the combination of both textual and visual features. This paper plans to develop the intelligent CBIR system for the collection of different benchmark texture datasets. Here, a new descriptor named Information Oriented Angle-based Local Tri-directional Weber Patterns (IOA-LTriWPs) is adopted. The pattern is operated not only based on tri-direction and eight neighborhood pixels but also based on four angles [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]. Once the patterns concerning tri-direction, eight neighborhood pixels, and four angles are taken, the best patterns are selected based on maximum mutual information. Moreover, the histogram computation of the patterns provides the final feature vector, from which the new weighted feature extraction is performed. As a new contribution, the novel weight function is optimized by the Improved MVO on random basis (IMVO-RB), in such a way that the precision and recall of the retrieved image is high. Further, the proposed model has used the logarithmic similarity called Mean Square Logarithmic Error (MSLE) between the features of the query image and trained images for retrieving the concerned images. The analyses on diverse texture image datasets have validated the accuracy and efficiency of the developed pattern over existing.

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