Content-based Image Retrieval Using Color Difference Histogram in Image Textures

Wireless capsule endoscopy (WCE) is a powerful tool for the diagnosis of gastrointestinal diseases. The output of this tool is in video with a length of about eight hours, containing about 8000 frames. It is a difficult task for a physician to review all of the video frames. In this paper, a new abnormality detection system for WCE images is proposed. The proposed system has four main steps: (1) preprocessing, (2) region of interest (ROI) extraction, (3) feature extraction, and (4) classification. In ROI extraction, at first, distinct areas are highlighted and nondistinct areas are faded by using the joint normal distribution; then, distinct areas are extracted as an ROI segment by considering a threshold. The main idea is to extract abnormal areas in each frame. Therefore, it can be used to extract various lesions in WCE images. In the feature extraction step, three different types of features (color, texture, and shape) are employed. Finally, the features are classified using the support vector machine. The proposed system was tested on the Kvasir-Capsule dataset. The proposed system can detect multiple lesions from WCE frames with high accuracy.

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“…LBP is one of the famous methods for extracting texture features [ 17 , 18 ]. The uniform LBP [ 19 ] is a rotation-invariant version of LBP.…”

Section: Proposed Methodsmentioning

confidence: 99%

A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images

Amiri

Hassanpour

Beghdadi

2021

Journal of Healthcare Engineering

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“…The proposed method is compared with the existing Content based Image Retrieval (CBIR) methods such as HCSF [3], CDH [7], MSD [9], and the methods based on colour and texture [10], as well as LMDEP [11] and CDHT [12]. The results show ability of the proposed method compared to the other existing CBIR methods.…”

Section: Figure 1 Results Of Various Image Retrieval Methods On a Query Imagementioning

confidence: 98%

“…Ajam et al in [12] used a data mining method along with low-level features for image retrieval. In this paper, the researchers proposed a CBIR system that extracts colour difference histogram in the texture image.…”

Section: Related Workmentioning

confidence: 99%

Content-based Image Retrieval Considering Colour Difference Histogram of Image Texture and Edge Orientation

AlyanNezhadi

Qazanfari

Ajam³

et al. 2020

IJE

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Content-based image retrieval is one of the interesting subjects in image processing and machine vision. In image retrieval systems, the query image is compared with images in the database to retrieve images containing similar content. Image comparison is done using features extracted from the query and database images. In this paper, the features are extracted based on the human visual system. Since the human visual system considers the texture and the edge orientation in images for comparison, the colour difference histogram associated with the image's texture and edge orientation is extracted as a feature. In this paper, the features are selected using the Shannon entropy criterion. The proposed method is tested using the Corel-5K and Corel-10K databases. The precision and recall criteria were used to evaluate the proposed system. The experimental results show the ability of the proposed system for more accurate retrieval rather than recently content-based image retrieval systems.

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“…Uniform Local Binary Pattern (uniform-LBP) [30] is used to extract texture features from ROI (non-ROI areas are set to zero). LBP is a famous method [31] [32] for feature extraction that is used in many WCE abnormality detection methods [33,34]. In the LBP algorithm, eight-pixels with a radius of one around the pixel are considered as the neighbors.…”

Section: Feature Extractionmentioning

confidence: 99%

Combining Deep Features and Hand-crafted Features for Abnormality Detection in WCE Images

Amiri

Hassanpour

Beghdadi

2022

Preprint

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In this paper, a computer-aided method is proposed for abnormality detection Wireless Capsule Endoscopy (WCE) video frames. Common abnormalities in WCE images include ulcers, bleeding, Angiodysplasia, Lymphoid Hyperplasia, and polyp. In this paper, deep features and Hand-crafted features are combined to detect these abnormalities in WCE images. There are no sufficient images to train deep structures therefore the ResNet50 pertained model is used to extract deep features. Hand-crafted features are associated with color, shape, and texture. They are extracted from the region of interest (ROI), i.e. suspicious region. The expectation Maximization (EM) algorithm is used to extract more distinct areas in the background as ROI. The expectation Maximization (EM) algorithm is configured in a way that can extract the area with a distinct texture and color as ROI. The EM algorithm is also initialized with a new fast method which leads to an increase in the accuracy of the method. We also used a novel idea to reveal unexpected color changes in the background due to existing lesions as a feature set. A large number of features are created by the method, so the minimum redundancy maximum relevance approach is used to select a subset of more effective features. These selected features are then fed to a Support Vector Machine for classification. The results show that the proposed approach can detect mentioned abnormalities in WCE frames with the accuracy of 97.82%

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Content-based Image Retrieval Using Color Difference Histogram in Image Textures

Cited by 14 publications

References 17 publications

A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images

A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images

Content-based Image Retrieval Considering Colour Difference Histogram of Image Texture and Edge Orientation

Combining Deep Features and Hand-crafted Features for Abnormality Detection in WCE Images

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