Medical Image Retrieval Using Multi-Texton Assignment

Tang, Qiling; Yang, Jun; Xia, Xianfu

doi:10.1007/s10278-017-0017-z

Cited by 8 publications

(3 citation statements)

References 42 publications

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“…The quantity marginalization is used to obtain the final energy score for each feature which is expressed in the equation (15).…”

Section: Infinite Feature Selectionmentioning

confidence: 99%

“…The Content Based Image Retrieval (CBIR) is developed to overcome the aforesaid limitation wherein the medical data of patients are combined with previous experiences of similar cases during the clinical decision-making process. Thus, it is advantageous to explore the images of the same cases from image archives thereby supporting the diagnoses (14) (15) (16) . Moreover, CBIR considers only visual contents of the image during the retrieval process and it avoids the associated semantic information of the image (17) (18) .…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Medical Image Retrieval and Classification using Deep Neural Network

Chethan¹

2020

IJST

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Background/Objectives: The main objective of this work is to obtain an efficient brain tumor image retrieval and classification using Deep Neural Network (DNN). Methods/Statistical analysis: The features from the medical images are extracted by using tamura feature extraction, Local Ternary Pattern (LTP) and Histogram of Oriented Gradients (HOG). Subsequently, an Infinite Feature Selection (Inf-FS) technique is incorporated to select optimum features from feature vector, which leads to improve the classification process using sparse auto encoder based DNN. Furthermore, the retrieval performance of the proposed method is improved by Euclidean Distance technique. Findings: An Open Access Series of Imaging Studies (OASIS) and Contrast Enhanced- Magnetic Resonance Image (CE-MRI) datasets are utilized to analyze the proposed method. The sparse auto encoder based DNN classification scheme yields an overall accuracy of 95.34% in OASIS dataset and 99.87% in CEMRI dataset with improved sensitivity, specificity, error rate. The retrieval performance of proposed technique is assessed in terms of Average Retrieval Precision (ARP) and compared with two existing methods such as Local Mesh Vector Co-occurrence Pattern (LMVCoP) and Content Based Image Retrieval- Convolutional Neural Network (CBIR-CNN). The ARP of the proposed method for CE-MRI and OASIS dataset is 98.33% and 88.25% that is high when compared to the CBIR-CNN, LMVCoP method. Novelty/Applications: An appropriate feature selection using Inf-FS and DNN based nonlinear feature data classification are used in the applications of medical image retrieval.

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“…The quantity marginalization is used to obtain the final energy score for each feature which is expressed in the equation (15).…”

Section: Infinite Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient Medical Image Retrieval and Classification using Deep Neural Network

Chethan¹

2020

IJST

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“…In fact, the CNN methodologies produces impressive results every field of vision. One of the most popular dataset of X-ray images (IRMA [23]) are examined for classification and segmentation system [24,25,26]. The unbalanced distribution in dataset is the most critical point.…”

Section: Introductionmentioning

confidence: 99%

A Heteromorphous Deep CNN Framework for Medical Image Segmentation Using Local Binary Pattern

Iqbal

Qureshi

2022

IEEE Access

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Estimating mitotic nuclei in breast cancer samples can aid in determining the tumor's aggressiveness and grading system. Because of their strong resemblance to non-mitotic nuclei and heteromorphic form, automated evaluation of mitotic nuclei is difficult. This study presents the BreastUNet, a new heteromorphous Deep Convolutional Neural Network (CNN) with feature grafting approach for analysing mitotic nuclei in breast histopathology images. In the first stage, the proposed method identifies probable mitotic patches in histopathological imaging regions, and in the second stage, the proposed model classifies these patches into mitotic and non-mitotic nuclei. For the building of a heteromorphous deep CNN, four distinct deep CNNs are developed and used as the basis CNN model. Deep CNNs with various architectural designs capture the structural, textural, and morphological aspects of mitotic nuclei. The performance of the proposed BreastUNet model is compared to those of state-of-the-art CNNs. The proposed model looks to be superior on the test set, with an F1 score of 0.95, Sensitivity and Specificity is 0.95 and area under the precision curve of 0.95. The recommended hybrid high F1 score and precision, as well as its excellent generalization and accuracy, imply that it might be used to build a pathologist's aid tool.

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