An efficient and robust approach for biomedical image retrieval using Zernike moments

Kumar, Yogesh; Aggarwal, Ashutosh; Tiwari, Shailendra; Singh, Karamjeet

doi:10.1016/j.bspc.2017.08.018

Cited by 43 publications

(17 citation statements)

References 49 publications

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“…Carneiro et al [22] proposed an automated mammogram analysis method based on deep learning to estimate the risk of patients of developing breast cancer. Kumar et al [23] presented an image retrieval system using Zernike moments (ZMs) for extracting features since the features can affect the effectiveness and efficiency of a breast CAD system. Aličković and Subasi [24] proposed a breast CAD method, in which genetic algorithms are used for extraction of informative and significant features, and the rotation forest is used to make a decision for two different categories of subjects with or without breast cancer.…”

Section: Related Workmentioning

confidence: 99%

Breast Cancer Detection Using Extreme Learning Machine Based on Feature Fusion With CNN Deep Features

et al. 2019

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A computer-aided diagnosis (CAD) system based on mammograms enables early breast cancer detection, diagnosis, and treatment. However, the accuracy of the existing CAD systems remains unsatisfactory. This paper explores a breast CAD method based on feature fusion with convolutional neural network (CNN) deep features. First, we propose a mass detection method based on CNN deep features and unsupervised extreme learning machine (ELM) clustering. Second, we build a feature set fusing deep features, morphological features, texture features, and density features. Third, an ELM classifier is developed using the fused feature set to classify benign and malignant breast masses. Extensive experiments demonstrate the accuracy and efficiency of our proposed mass detection and breast cancer classification method. INDEX TERMS Mass detection, computer-aided diagnosis, deep learning, fusion feature, extreme learning machine.

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Section: Related Workmentioning

confidence: 99%

Breast Cancer Detection Using Extreme Learning Machine Based on Feature Fusion With CNN Deep Features

et al. 2019

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“…To improve the competence of a breast-cancer CAD system, Kumar et al . [ 12 ] offered Zernike moments (ZMs) image retrieval system. To perform segmentation of breast tumors, Saidin et al .…”

Section: Related Workmentioning

confidence: 99%

A Novel Lightweight Deep Learning-Based Histopathological Image Classification Model for IoMT

Gupta¹,

Sharma

Ahmed

et al. 2021

Neural Process Lett

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The unavailability of appropriate mechanisms for timely detection of diseases and successive treatment causes the death of a large number of people around the globe. The timely diagnosis of grave diseases like different forms of cancer and other life-threatening diseases can save a valuable life or at least extend the life span of an afflicted individual. The advancement of the Internet of Medical Things (IoMT) enabled healthcare technologies can provide effective medical facilities to the population and contribute greatly towards the recuperation of patients. The usage of IoMT in the diagnosis and study of histopathological images can enable real-time identification of diseases and corresponding remedial actions can be taken to save an affected individual. This can be achieved by the use of imaging apparatus with the capacity of auto-analysis of captured images. However, most deep learning-based image classifying models are bulk in size and are inappropriate for use in IoT based imaging devices. The objective of this research work is to design a deep learning-based lightweight model suitable for histopathological image analysis with appreciable accuracy. This paper presents a novel lightweight deep learning-based model "ReducedFireNet", for auto-classification of histopathological images. The proposed method attained a mean accuracy of 96.88% and an F1 score of 0.968 on evaluating an actual histopathological image data set. The results are encouraging, considering the complexity of histopathological images. In addition to the high accuracy the lightweight design (size in few KBs) of the ReducedFireNet model, makes it suitable for IoMT imaging equipment. The simulation results show the proposed model has computational requirement of 0.201 GFLOPS and has a mere size of only 0.391 MB.

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“…Zernike moments have a wide range of applications in different fields such as image recognition and classification [20,25,27,28], copy-move-forgery detection [29][30][31][32][33], video-forgery detection [34], watermark detection [35][36][37], and medical-image retrieval [38]. In the copy-move forgery-detection problem, Zernike moment features-based methods notably showed impressive performances to different kinds of transformations in comparison with other approaches [29][30][31].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Image-Retrieval Method for Image-Splicing Validation

et al. 2019

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Recently, the task of validating the authenticity of images and the localization of tampered regions has been actively studied. In this paper, we go one step further by providing solid evidence for image manipulation. If a certain image is proved to be the spliced image, we try to retrieve the original authentic images that were used to generate the spliced image. Especially for the image retrieval of spliced images, we propose a hybrid image-retrieval method exploiting Zernike moment and Scale Invariant Feature Transform (SIFT) features. Due to the symmetry and antisymmetry properties of the Zernike moment, the scaling invariant property of SIFT and their common rotation invariant property, the proposed hybrid image-retrieval method is efficient in matching regions with different manipulation operations. Our simulation shows that the proposed method significantly increases the retrieval accuracy of the spliced images.

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An efficient and robust approach for biomedical image retrieval using Zernike moments

Cited by 43 publications

References 49 publications

Breast Cancer Detection Using Extreme Learning Machine Based on Feature Fusion With CNN Deep Features

Breast Cancer Detection Using Extreme Learning Machine Based on Feature Fusion With CNN Deep Features

A Novel Lightweight Deep Learning-Based Histopathological Image Classification Model for IoMT

Hybrid Image-Retrieval Method for Image-Splicing Validation

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