A new approach for content-based image retrieval for medical applications using low-level image descriptors

Shatnawi, Mohammed Q.; Al-Rousan, Mohammad; Amareen, Suzan

doi:10.11591/ijece.v10i4.pp4363-4371

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…In this approach, student or a medical specialist is assumed to be a user and the query is a medical image. The average matching accuracy was 70 % [16].…”

Section: Related Workmentioning

confidence: 99%

A Smart Content-Based Image Retrieval Approach Based on Texture Feature and Slantlet Transform

Mhaibes

Shallal

Abood

2022

Int. j. electr. comput. eng. syst. (Online)

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With the advancement of digital storing and capturing technologies in recent years, an image retrieval system has been widely known for Internet usage. Several image retrieval methods have been proposed to find similar images from a collection of digital images to a specified query image. Content-based image retrieval (CBIR) is a subfield of image retrieval techniques that extracts features and descriptions content such as color, texture, and shapes from a huge database of images. This paper proposes a two-tier image retrieval approach, a coarse matching phase, and a fine-matching phase. The first phase is used to extract spatial features, and the second phase extracts texture features based on the Slantlet transform. The findings of this study revealed that texture features are reliable and capable of producing excellent results and unsusceptible to low resolution and proved that the SLT-based texture feature is the perfect mate. The proposed method's experimental results have outperformed the benchmark results with precision gaps of 28.0 % for the Caltech 101 dataset. The results demonstrate that the two-tier strategy performed well with the successive phase (fine-matching) and the preceding phase (coarse matching) working hand in hand harmoniously.

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“…In this approach, student or a medical specialist is assumed to be a user and the query is a medical image. The average matching accuracy was 70 % [16].…”

Section: Related Workmentioning

confidence: 99%

A Smart Content-Based Image Retrieval Approach Based on Texture Feature and Slantlet Transform

Mhaibes

Shallal

Abood

2022

Int. j. electr. comput. eng. syst. (Online)

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“…Precision can be defined as the ratio between the number of correctly predicted images and the total number of predicted images. The formula used can be seen in equation 14 [18].…”

Section: Performance Testingmentioning

confidence: 99%

“…Recall or also known as sensitivity can be calculated as the probability that the image that is correctly predicted (true positive) is relevant. The formula used can be seen in equation 15 [18].…”

Section: Pr = True Positivementioning

confidence: 99%

Big Cats Classification Based on Body Covering

Pratama

Maki

Sthevanie

2021

RESTI

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The reduced habitat owned by an animal has a very bad impact on the survival of the animal, resulting in a continuous decrease in the number of animal populations especially in animals belonging to the big cat family such as tigers, cheetahs, jaguars, and others. To overcome the decline in the animal population, a classification model was built to classify images that focuses on the pattern of body covering possessed by animals. However, in designing an accurate classification model with an optimal level of accuracy, it is necessary to consider many aspects such as the dataset used, the number of parameters, and computation time. In this study, we propose an animal image classification model that focuses on animal body covering by combining the Pyramid Histogram of Oriented Gradient (PHOG) as the feature extraction method and the Support Vector Machine (SVM) as the classifier. Initially, the input image is processed to take the body covering pattern of the animal and converted it into a grayscale image. Then, the image is segmented by employing the median filter and the Otsu method. Therefore, the noise contained in the image can be removed and the image can be segmented. The results of the segmentation image are then extracted by using the PHOG and then proceed with the classification process by implementing the SVM. The experimental results showed that the classification model has an accuracy of 91.07%.

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“…Data histogram [20][21][22][23] is an array of elements, each of which points to the repetition of one value in the data set [24][25][26][27]. Calculating the wave file histogram is an initial task of the proposed later in this paper method of features extraction.…”

Section: Wave File Histogrammentioning

confidence: 99%

A flexible method to create wave file features

Qaryouti

2021

IJECE

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Digital audio signal is one of the most important data type at present, it is used in various vital applications, such as human knowledge, security and banking applications, most applications require signal identification and recognition, and to increase the efficiency of these applications we must seek a method to represent the audio file by a small set of values called a features vector. In this paper research we will introduce an enhanced method of features extraction based on k-mean clustering. The method will be tested and implemented to show how the proposed method can reduce the efforts of voice identification, and can minimize the recognition time a set of voice extracted features must be used instead of using the voice wave file.

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A new approach for content-based image retrieval for medical applications using low-level image descriptors

Cited by 4 publications

References 15 publications

A Smart Content-Based Image Retrieval Approach Based on Texture Feature and Slantlet Transform

A Smart Content-Based Image Retrieval Approach Based on Texture Feature and Slantlet Transform

Big Cats Classification Based on Body Covering

A flexible method to create wave file features

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