Logarithmic Fuzzy Entropy Function for Similarity Measurement in Multimodal Medical Images Registration

Multimodal medical images are useful for observing tissue structure clearly in clinical practice. To integrate multimodal information, multimodal registration is significant. The entropy-based registration applies a structure descriptor set to replace the original multimodal image and compute similarity to express the correlation of images. The accuracy and converging rate of the registration depend on this set. We propose a new method, logarithmic fuzzy entropy function, to compute the descriptor set. It is obvious that the proposed method can increase the upper bound value from log(r) to log(r) + ∆(r) so that a more representative structural descriptor set is formed. The experiment results show that our method has faster converging rate and wider quantified range in multimodal medical images registration.

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Thresholding Selection Based on Fuzzy Entropy and Bee Colony Algorithm for Image Segmentation

Intelligent Image and Video Interpretation ◽

10.4018/978-1-4666-3958-4.ch006 ◽

2013 ◽

pp. 165-183

Author(s):

Yonghao Xiao ◽

Weiyu Yu ◽

Jing Tian

Keyword(s):

Image Segmentation ◽

Fuzzy Entropy ◽

Single Parameter ◽

Entropy Function ◽

Experimental Results ◽

Superior Performance ◽

Image Thresholding ◽

Minimum Function ◽

Minimum Value ◽

Bee Colony

Image thresholding segmentation based on Bee Colony Algorithm (BCA) and fuzzy entropy is presented in this chapter. The fuzzy entropy function is simplified with single parameter. The BCA is applied to search the minimum value of the fuzzy entropy function. According to the minimum function value, the optimal image threshold is obtained. Experimental results are provided to demonstrate the superior performance of the proposed approach.

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A Novel Fuzzy Entropy-Based Method to Improve the Performance of the Fuzzy C-Means Algorithm

Electronics ◽

10.3390/electronics9040554 ◽

2020 ◽

Vol 9 (4) ◽

pp. 554 ◽

Cited By ~ 1

Author(s):

Barbara Cardone ◽

Ferdinando Di Martino

Keyword(s):

Machine Learning ◽

Clustering Algorithm ◽

Optimal Solution ◽

Fuzzy Entropy ◽

Entropy Function ◽

Fuzzy C Means ◽

Fuzzy C Means Clustering ◽

Random Initialization ◽

Fuzzy C Means Algorithm ◽

Optimal Cluster

One of the main drawbacks of the well-known Fuzzy C-means clustering algorithm (FCM) is the random initialization of the centers of the clusters as it can significantly affect the performance of the algorithm, thus not guaranteeing an optimal solution and increasing execution times. In this paper we propose a variation of FCM in which the initial optimal cluster centers are obtained by implementing a weighted FCM algorithm in which the weights are assigned by calculating a Shannon Fuzzy Entropy function. The results of the comparison tests applied on various classification datasets of the UCI Machine Learning Repository show that our algorithm improved in all cases relating to the performances of FCM.

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MP20: ImageSim - performance-based medical image interpretation learning system

CJEM ◽

10.1017/cem.2018.174 ◽

2018 ◽

Vol 20 (S1) ◽

pp. S47-S47

Author(s):

K. Boutis ◽

M. Pecarcic ◽

M. Pusic

Keyword(s):

Emergency Medicine ◽

Clinical Practice ◽

Medical Image ◽

Medical Images ◽

Image Interpretation ◽

Deliberate Practice ◽

Learning System ◽

Point Of Care Ultrasound ◽

Emergency Physicians ◽

Emergency Medicine Physician

Introduction: Medical images (e.g. radiographs) are the most commonly ordered tests in emergency medicine. As such, emergency medicine physicians are faced with the task of learning the skill of interpreting these images to an expert performance level by the time they provide opinions that guide patient management decisions. However, discordant interpretations of these images between emergency physicians and expert counterparts (e.g. radiologists) is a common cause of medical error. In pediatrics, this problem is even greater due to the changing physiology with age. Methods: ImageSim (https://imagesim.com/) is an evidence-based on-line learning platform derived and validated over an 11 year period (https://imagesim.com/research-and-efficacy/). This learning system incorporates the concepts of cognitive simulation, gamification, deliberate practice, and performance-based competency in the presentation and interpretation of medical images. Specifically, ImageSim presents images as they are experienced in clinical practice and incorporates a normal to abnormal ratio is representative of that seen in emergency medicine. Further, it forces the participant to commit to the case being normal or abnormal and if abnormal, the participant has to visually locate the specific area of pathology on the image. The participant submits a response and gets text and visual feedback with every case. After each case, the participant gets to play again until they reach a desired competency threshold (80% is bronze resident; 90% silver staff emergency medicine physician; 97% gold radiologist). Importantly, the learning experience also emphasizes deliberate practice such that the learning system provides hundreds of case examples and therefore each participants performance has the opportunity to improve along their individual learning curve. Results: Course selection was made based on known medical image interpretation knowledge gaps for practicing emergency physicians. Currently, ImageSim live courses include pediatric musculoskeletal radiographs (2,100 cases, 7 modules) and pediatric chest radiographs (434 cases). In 2018, we will also release a pediatric point-of-care ultrasound course (400 cases, 4 modules) and the pre-pubertal female genital examination (150 cases). For a demo, go to https://imagesim.com/demo/. Using ImageSim, the deliberate practice of about 120 cases (1 hour time commitment) increases accuracy on average by 15%. Currently integrated into 10 emergency medicine training programs and there are about 300 continuing medical education world-wide participants. Conclusion: While acquiring mastery for these images may take years to acquire via clinical practice alone, this learning system can potentially help achieve this in just a few hours.

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Multi-Modal Content Based Image Retrieval in Healthcare

New Technologies for Advancing Healthcare and Clinical Practices ◽

10.4018/978-1-60960-780-7.ch003 ◽

2011 ◽

pp. 44-59 ◽

Cited By ~ 1

Author(s):

Jinman Kim ◽

Ashnil Kumar ◽

Tom Weidong Cai ◽

David Dagan Feng

Keyword(s):

Feature Extraction ◽

Clinical Practice ◽

Image Retrieval ◽

User Interaction ◽

Content Based Image Retrieval ◽

Similarity Measurement ◽

Application Domain ◽

Pet Ct ◽

The Future ◽

Future Challenges

Multi-modal imaging requires innovations in algorithms and methodologies in all areas of CBIR, including feature extraction and representation, indexing, similarity measurement, grouping of similar retrieval results, as well as user interaction. In this chapter, we will discuss the rise of multi-modal imaging in clinical practice. We will summarize some of our pioneering CBIR achievements working with these data, exemplified by a specific application domain of PET-CT. We will also discuss the future challenges in this significantly important emerging area.

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Decibel Imaging for Generating Medical Report using Ultrasonic

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.d8309.049420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 2444-2448

Keyword(s):

Clinical Practice ◽

Medical Imaging ◽

Medical Images ◽

Attention Mechanism ◽

Medical Report ◽

Automated Generation ◽

Report Writing ◽

Learning Framework ◽

Complete Report ◽

Types Of Information

Medical imaging is commonly used for diagnosis and care in clinical practice. Report-writing would be prone to mistakes for inexperienced physicians, and experienced physicians would be time consuming and boring. To handle these issues, we study the automated generation of medical imaging reports. This task presents several challenges. First, a complete report contains multiple heterogeneous types of information including findings and tags. Second, abnormal regions in medical images are difficult to spot. Third, usually, the reports are lengthy and contain multiple sentences. To deal with these challenges, we (1) build a multi-task learning framework which jointly performs the prediction of tags and therefore the generation of paragraphs, (2) propose a co-attention mechanism to localize regions containing abnormalities and generate narrations for them, (3) develop a hierarchical LSTM model to get long paragraphs. We show the efficacy of the proposed methods on two datasets which are publicly accessible.

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Multi-Trend Structure Descriptor at Micro-Level for Histological Image Retrieval

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c6120.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 7539-7543 ◽

Cited By ~ 1

Keyword(s):

Image Retrieval ◽

Medical Image ◽

Image Representation ◽

Image Data ◽

Similarity Measurement ◽

Challenging Problem ◽

Histological Image ◽

Illumination Changes ◽

Micro Level ◽

Structure Descriptor

Since hospitals are generating and using image data extensively, medical image databases and its size are rising rapidly. This led to difficulties in browsing and managing the huge databases. Therefore, the necessity for the development of efficient content-based medical image retrieval (CBMIR) system arises and is more challenging problem for researchers. In this paper, to alleviate the unbalanced distribution of image representation using multi-trend structure descriptor (MTSD), MTSD is computed at micro level i.e., image is divided into number of sub-images and for each sub-image MTSD is exploited. In similarity measurement, we compared the MTSDs of corresponding sub-images in query and target images than the liner ordered collection of smallest similarity values between the sub-images are considered for retrieval. Experiments revels that computation of proposed feature at micro level retains the localized representation and considering the liner ordered collection of smallest similarity values between the sub-images provides consistency under illumination changes and noise and thus proposed CBMIR achieves better results.

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