Content Based Medical Image Retrieval Based on Salient Regions Combined with Deep Learning

Tuyet, Vo Thi Hong; Binh, Nguyen Thanh; Quoc, Nguyen Kim; Khare, Ashish

doi:10.1007/s11036-021-01762-0

Cited by 17 publications

(14 citation statements)

References 25 publications

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“…The dataset includes 5 sets of medical images with varying quality from low to high. By using the best medical image quality group, precision has an accuracy of 91.61%, and recall has an accuracy of 89.61% [ 8 ]. Sboev analyzes the stages of creation, testing and practical implementation of the modern organizational, and personnel structure of mobile medical teams and their function to meet the needs of the peaceful population of Moscow during the prehospital period in Moscow.…”

Section: Related Workmentioning

confidence: 99%

Evaluation and Correlation Analysis of Mental and Psychological Factors and Premature Ejaculation in Patients with Benign Prostatic Hyperplasia in Mobile Medical System

Peng

2022

Applied Bionics and Biomechanics

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Chronic prostatitis is a very common and very difficult disease. Based on the mobile medical system, this paper carried out a correlation analysis on the psychological factors and the evaluation of premature ejaculation in patients with benign prostatic hyperplasia. The article first analyzes the application in the field of mobile medical and then introduces the prostate image segmentation method based on the geometric active contour model. The emergence of automatic organ tissue segmentation technology is timely; it can help clinicians save a lot of manual segmentation time and has better reversibility and objectivity, and the theory of curve evolution is analyzed. Finally, this paper introduces the experimental research object and purpose, makes a statistical analysis of the symptoms of benign prostatic hyperplasia, and compares the incidence of psychological symptoms in patients with prostatitis under different factors. The experimental results of this paper show that 90% of prostatitis patients have mild psychological problems, and 10% have moderate psychological problems. Among them, the main reason for the psychological disorder of patients is depression, for which we should strengthen the care for patients.

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Section: Related Workmentioning

confidence: 99%

Evaluation and Correlation Analysis of Mental and Psychological Factors and Premature Ejaculation in Patients with Benign Prostatic Hyperplasia in Mobile Medical System

Peng

2022

Applied Bionics and Biomechanics

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“…The quantized histogram is then generated in order to obtain global colour information in the spatial domain. Tuyet et al [26] had introduced a content based medical image retrieval based on salient regions combined with deep learning. This method contained two stages such as, the first stage is based on an offline task to extract local object features and the second stage is responsible for an online take for content-based image retrieval in the database.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep image retrieval using artificial neural network interpolation and indexing based on similarity measurement

Ahmad

2022

CAAI Trans on Intel Tech

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In content-based image retrieval (CBIR), primitive image signatures are critical because they represent the visual characteristics. Image signatures, which are algorithmically descriptive and accurately recognized visual components, are used to appropriately index and retrieve comparable results. To differentiate an image in the category of qualifying contender, feature vectors must have image information's like colour, objects, shape, spatial viewpoints. Previous methods such as sketch-based image retrieval by salient contour (SBIR) and greedy learning of deep Boltzmann machine (GDBM) used spatial information to distinguish between image categories. This requires interest points and also feature analysis emerged image detection problems. Thus, a proposed model to overcome this issue and predict the repeating pattern as well as series of pixels that conclude similarity has been necessary. In this study, a technique called CBIR-similarity measure via artificial neural network interpolation (CBIR-SMANN) has been presented. By collecting datasets, the images are resized then subject to Gaussian filtering in the pre-processing stage, then by permitting them to the Hessian detector, the interesting points are gathered. Based on Skewness, mean, kurtosis and standard deviation features were extracted then given to ANN for interpolation. Interpolated results are stored in a database for retrieval. In the testing stage, the query image was inputted that is subjected to pre-processing, and feature extraction was then fed to the similarity measurement function. Thus, ANN helps to get similar images from the database. CBIR-SMANN have been implemented in the python tool and then evaluated for its performance. Results show that CBIR-SMANN exhibited a high recall value of 78% with a minimum retrieval time of 980 ms. This showed the supremacy of the proposed model was comparatively greater than the previous ones.

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“…Kasban et al [15] developed a robust CBMIR system based on the combination of the image encoding techniques. Recently, Tuyet et al [16] applied the deep learning techniques to the salient region-based CBMIR scheme. Due to the limited system scalability, the retrieval performances of the aforementioned single-PC-based CBMIR systems are not satisfactory, especially when dealing with enormous volumes of medical images [17].…”

Section: Pr Jmentioning

confidence: 99%

Progressive Distributed and Parallel Similarity Retrieval of Large CT Image Sequences in Mobile Telemedicine Networks

Zhuang

Jiang

2022

Wireless Communications and Mobile Computing

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Computed tomography image (CTI) sequence is essentially a time-series data that typically consists of a large amount of nearby and similar CTIs. Due to the high communication and computational costs, it is difficult to perform a progressive distributed similarity retrieval of the large CTI sequence (CTIS)s, particularly in resource-constraint mobile telemedicine network (MTN)s. In this paper, we present a Dprs method—progressive distributed and parallel similarity retrieval scheme for the CTISs in the MTN. To the best of our knowledge, there is little research on the Dprs processing, especially in the MTN. Four supporting techniques (i.e., (1) PCTI-based similarity measurement, (2) lightweight privacy-preserving strategy, (3) SSL-based data distribution scheme, and (4) the UDI framework) are developed. The experimental evaluation indicates that our proposed Dprs method is more progressive than the state of the art, with a significant reduction in response time.

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Content Based Medical Image Retrieval Based on Salient Regions Combined with Deep Learning

Cited by 17 publications

References 25 publications

Evaluation and Correlation Analysis of Mental and Psychological Factors and Premature Ejaculation in Patients with Benign Prostatic Hyperplasia in Mobile Medical System

Evaluation and Correlation Analysis of Mental and Psychological Factors and Premature Ejaculation in Patients with Benign Prostatic Hyperplasia in Mobile Medical System

Deep image retrieval using artificial neural network interpolation and indexing based on similarity measurement

Progressive Distributed and Parallel Similarity Retrieval of Large CT Image Sequences in Mobile Telemedicine Networks

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