Manhattan distance-based histogram of oriented gradients for content-based medical image retrieval

Veerashetty, Sachinkumar; Patil, Nagaraj B.

doi:10.1080/1206212x.2019.1653011

Cited by 15 publications

(7 citation statements)

References 18 publications

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“…Veerashetty and Patil [ 27 ] used Gaussian filter for medical image enhancement, and then feature extraction was performed using Manhattan distance-based HOG (MHOG) for extracting the feature vectors from the denoised image. Lastly, Euclidean distance measure was used for similarity matching between the extracted feature vectors for relevant medical image retrieval.…”

Section: Related Workmentioning

confidence: 99%

Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model

Praveena¹,

Guptha²,

Kazemzadeh

et al. 2022

Journal of Healthcare Engineering

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In recent times, a large number of medical images are generated, due to the evolution of digital imaging modalities and computer vision application. Due to variation in the shape and size of the images, the retrieval task becomes more tedious in the large medical databases. So, it is essential in designing an effective automated system for medical image retrieval. In this research study, the input medical images are acquired from new Pap smear dataset, and then, the visible quality of acquired medical images is improved by applying image normalization technique. Furthermore, the hybrid feature extraction is accomplished using histogram of oriented gradients and modified local binary pattern to extract the color and texture feature vectors that significantly reduces the semantic gap between the feature vectors. The obtained feature vectors are fed to the independent condensed nearest neighbor classifier to classify the seven classes of cell images. Finally, relevant medical images are retrieved using chi square distance measure. Simulation results confirmed that the proposed model obtained effective performance in image retrieval in light of specificity, recall, precision, accuracy, and f-score. The proposed model almost achieved 98.88% of retrieval accuracy, which is better compared to other deep learning models such as long short-term memory network, deep neural network, and convolutional neural network.

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Section: Related Workmentioning

confidence: 99%

Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model

Praveena¹,

Guptha²,

Kazemzadeh

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

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“…A CBMIR method known as Manhattan-distancebased Histogram of Oriented Gradients (M-HOG) was given by Ahmed et al [12], in which the RGB-based images that were collected were first converted into Hue Saturation Value (HSV) colour space. A group of 18 features was extracted using feature colour and texture function of Grey Level Co-occurrence Matrix (GLCM), and comparable scores were computed.…”

Section: Literature Reviewmentioning

confidence: 99%

Feature Extraction Method using HoG with LTP for Content-Based Medical Image Retrieval

Shamna¹,

Musthafa²

2023

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

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An accurate diagnosis is significant for the treatment of any disease in its early stage. Content-Based Medical Image Retrieval (CBMIR) is used to find similar medical images in a huge database to help radiologists in diagnosis. The main difficulty in CBMIR is semantic gaps between the lower-level visual details, captured by computer-aided tools and higher-level semantic details captured by humans. Many existing methods such as Manhattan Distance, Triplet Deep Hashing, and Transfer Learning techniques for CBMIR were developed but showed lower efficiency and the computational cost was high. To solve such issues, a new feature extraction approach is proposed using Histogram of Gradient (HoG) with Local Ternary Pattern (LTP) to automatically retrieve medical images from the Contrast-Enhanced Magnetic Resonance Imaging (CE-MRI) database. Adam optimization algorithm is utilized to select features and the Euclidean measure calculates the similarity for query images. From the experimental analysis, it is clearly showing that the proposed HoG-LTP method achieves higher accuracy of 98.8%, a sensitivity of 98.5%, and a specificity of 99.416%, which is better when compared to the existing Random Forest (RF) method which displayed an accuracy, sensitivity, and specificity of 81.1%, 81.7% and 90.5% respectively.

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“…1. Add all the elements 2 P as given below: [54]. The maximum value of the objective function represents the existence of edges.…”

Section: Diffusion Stagementioning

confidence: 99%

SSII: Secured and High-Quality Steganography Using Intelligent Hybrid Optimization Algorithms for IoT

et al. 2021

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Internet of Things (IoT) is a domain where the transfer of big data is taking place in every single second. The security of these data is a challenging task; however, security challenges can be mitigated with cryptography and steganography techniques. These techniques are crucial when dealing with user authentication and data privacy. In the proposed work, a highly secured technique is proposed using IoT protocol and steganography. This work proposes an image steganography procedure by utilizing the combination of various algorithms that build the security of the secret data by utilizing Binary bit-plane decomposition (BBPD) based image encryption technique. Thereafter a Salp Swarm Optimization Algorithm (SSOA) based adaptive embedding process is proposed to increase the payload capacity by setting different parameters in the steganographic embedding function for edge and smooth blocks. Here the SSOA algorithm is used to localize the edge and smooth blocks efficiently. Then, the hybrid Fuzzy Neural Network with a backpropagation learning algorithm is used to enhance the quality of the stego images. Then these stego images are transferred to the destination in the highly secured protocol of IoT. The proposed steganography technique shows better results in terms of security, image quality and the payload capacity in comparison with the existing state of art methods.

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Manhattan distance-based histogram of oriented gradients for content-based medical image retrieval

Cited by 15 publications

References 18 publications

Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model

Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model

Feature Extraction Method using HoG with LTP for Content-Based Medical Image Retrieval

SSII: Secured and High-Quality Steganography Using Intelligent Hybrid Optimization Algorithms for IoT

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