Content-Based Medical Image Retrieval Using Delaunay Triangulation Segmentation Technique

Kugunavar, Sneha; Prabhakar, C. J.

doi:10.4018/jitr.2021040103

Cited by 12 publications

(8 citation statements)

References 28 publications

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“…Most of the developed CBMIR systems work based on image information such as edges, texture, color, and shape features are generally extracted from handcrafted feature extraction techniques [ 9 – 20 ]. Incompatibility between high- and low-level image features leads to “semantic gap” that affects the overall system performance by creating an ambiguity between the extracted feature vectors and the query image [ 21 – 23 ].…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…We collect a data point per cubic decimeter; then, we have 118059 data to deal with. Figure 3 shows the results of visualization simulation and Delaunay triangulation [44,45]. e first line shows the situation about two sound sources while the second line is about three.…”

Section: Sound Field Distributionmentioning

confidence: 99%

Detection, Integration, and Optimization of Acoustic Field Simulation in the Closed Space

Meng

Lin

Liang

2021

Scientific Programming

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Noise pollution in the closed space such as railway carriage is an important problem because the noise pollution seriously affects comfort and health of people in the closed space. We propose the method to detection, integration, and optimization of acoustic field simulation in the closed space. First, we analyze the acoustic ﬁeld distribution in the virtual 3D close space. We use harmonic sound wave propagation in the closed space and present the distribution according to geometric analysis. Second, we introduce Delaunay triangulation and k-means clustering into visualization to form the quiet zone and show it in 3D perspective. Our method used acoustic simulation to develop the sound barrier system. The simulation results show that our method can improve the analysis of the noise problem in the closed space.

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“…The image retrieval algorithm based on LBP can achieve better retrieval results, but the computational complexity is generally large and needs to be improved. Kugunavar and Prabhakar [13] proposed the gray level cooccurrence matrix method. The spatial texture of the image is described by the texture features such as moment of inertia, inertial state, inertial correlation coefficient, contrast fraction, and second moment angle.…”

Section: Related Workmentioning

confidence: 99%

Image Retrieval Model Analysis of Digital Library Based on Texture Characteristics

Zhao

2021

Advances in Mathematical Physics

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A new document image retrieval algorithm is proposed in view of the inefficient retrieval of information resources in a digital library. First of all, in order to accurately characterize the texture and enhance the ability of image differentiation, this paper proposes the statistical feature method of the double-tree complex wavelet. Secondly, according to the statistical characteristic method, combined with the visual characteristics of the human eye, the edge information in the document image is extracted. On this basis, we construct the meaningful texture features and use texture features to define the characteristic descriptors of document images. Taking the descriptor as the clue, the content characteristics of the document image are combined organically, and appropriate similarity measurement criteria are used for efficient retrieval. Experimental results show that the algorithm not only has high retrieval efficiency but also reduces the complexity of the traditional document image retrieval algorithm.

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Content-Based Medical Image Retrieval Using Delaunay Triangulation Segmentation Technique

Cited by 12 publications

References 28 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

Detection, Integration, and Optimization of Acoustic Field Simulation in the Closed Space

Image Retrieval Model Analysis of Digital Library Based on Texture Characteristics

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