Energy based Methods for Medical Image Segmentation

Juneja, Pamela; Kashyap, Ramgopal

doi:10.5120/ijca2016910808

Cited by 13 publications

(11 citation statements)

References 32 publications

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“…9 If preprocessing step is lost, it results in low learning efficiency, large computational complexity, and low quality of learning data rules when machine learning or mathematical statistics methods are used to process these data. One is the incompleteness of data due to the loss of some data or some important attributes.…”

Section: Multi-feature Ct Image Preprocessingmentioning

confidence: 99%

“…The last problem is mismatching between original and proposed data. 9 If preprocessing step is lost, it results in low learning efficiency, large computational complexity, and low quality of learning data rules when machine learning or mathematical statistics methods are used to process these data. Therefore, preprocessing step is added before data extraction to provide high-quality input data for following learning algorithm, as well as better results than using the original data.…”

Section: Multi-feature Ct Image Preprocessingmentioning

confidence: 99%

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Multi‐feature data mining for CT image recognition

Yong-lian

2018

Concurrency and Computation

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Recognition method of CT images based on color, morphology, and texture is inaccurate and unreliable recently. Therefore, a medical image recognition method based on data mining is proposed with a multi-feature fusion in this paper. First, image acquisition method is determined by analyzing the environment of CT image recognition. Then, acquired CT image is standardized and whitened to reduce redundant information. Moreover, based on feature of the color, texture, and height in preprocessed CT images, a deep neural network is trained by using a large amount of image data in normal scene. The deep learning classifier is fine-tuned by using the marked multi-feature CT image data. Finally, output recognition result is obtained according to the classification decision threshold. Experimental results show that the correct recognition rate of the proposed method can reach more than 98%. The accuracy rate is higher and the stability of proposed method is better by comparing with traditional CT image recognition methods. KEYWORDSCT images, data mining, deep learning, image recognition, multiple feature INTRODUCTIONIn recent years, with the rapid development of medical imaging technology, it improved to big medical image analysis. Today, many big database are constructed. How we can extract useful information from massive medical image data in the database has brought great challenges to medical image recognition since traditional database technology cannot provide enough service for the large database. Data mining is used to obtain effective information in medical image database. CT image recognition is a multidisciplinary cross-domain of comprehensive medical imaging, mathematical modeling, and computer technology. 1,2 Today, there are two new issues that are brought because of large scale of multi-feature CT image. One issue is CT image data that has a higher dimension and requires a model with stronger learning adaptability. The other is big CT image data that is more fragmented with more complex data structure, which requires integration of huge different information. 3 Such requirements cannot be solved by traditional data analysis methods. Therefore, how we extract useful information from massive CT image data has become a research hotspot. With the rapid development and popularization of CT imaging equipment, CT image data have increased explosively. How we can efficiently and accurately analyze CT image has become a major challenge. 4 Furthermore, computer-assisted CT medical image analysis improves the efficiency and accuracy of the analysis and diagnosis of CT image. Therefore, computer-aided detection and diagnosis are becoming a cross-sectional research area that receives more and more attention.A multi-feature CT medical image recognition method was proposed based on polarization spectrum. 5 The method used indoor imaging spectrometer FISS-P equipped with polarizers to collect the polarization spectrum images, including general images and five kinds of CT medical images indoors. They recognized CT images by ...

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Section: Multi-feature Ct Image Preprocessingmentioning

confidence: 99%

Section: Multi-feature Ct Image Preprocessingmentioning

confidence: 99%

Multi‐feature data mining for CT image recognition

Yong-lian

2018

Concurrency and Computation

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“…The advantage of fuzzy system is that they are easy to understand, as the membership function partition the dataspace properly [23]. Fuzzy clustering algorithms include FCM (fuzzy C means) algorithm, GK (Gustafson-Kessel), GMD (Gaussian mixture decomposition), FCV (Fuzzy C varieties), AFC, FCS, FCSS, FCQS, FCRS algorithm and etc, among all the FCM is the most accepted method since it can preserve much more information than other approaches [24].…”

Section: Feature Based Clusteringmentioning

confidence: 99%

“…Active contours or snakes are computer generated curves [22][23] that move within the image to find object boundaries under the influence of internal and external forces. Snake is placed near the contour of Region of interest (ROI).…”

Section: Energy Based Segmentationmentioning

confidence: 99%

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Fast Segmentation Methods for Medical Images

Upadhyay¹,

Kashyap²

2016

IJCA

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Image segmentation is about splitting the whole image into segments. In case of image analysis, image processing one of the crucial steps is segmentation of the image. Segmentation of image concern about dividing the entire image in sub parts that may be similar or dissimilar with respect to features. Output of image segmentation has consequence on analysis of image, further processing of images. Analysis of image comprises depiction of object and object representation, measurement of features. Therefore characterization, area of interest"s visualization in the image, description have crucial job in the segmentation of the image. This survey explains some methods of image segmentation. Segmenting an image into meaningful parts is a fundamental operation in image processing. Image segmentation is the process of partitioning a digital image into multiple segments. In this paper, various image segmentation methods are explained like edge detection, region based segmentation, neural network techniques ,energy based and hybrid methods, etc. In this paper review of image segmentation is explained by using different techniques. The efficiency of the segmentation process improved with the help of several algorithms, namely, active contour, level set, fuzzy clustering and K-means clustering. This paper analyses the performance of algorithms for image segmentation in detail. Intensity and texture based image segmentation is the two levels of the level set method. The combination of both intensity and texture based image segmentation provides better results than the traditional methods. The detailed survey of segmentation techniques provides the requirement of the suitable enhancement method that supports both intensity and texture based segmentation for better results.

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Micro‐segmentation of retinal image lesions in diabetic retinopathy using energy‐based fuzzy C‐Means clustering (EFM‐FCM)

Naz,

Nijhawan,

Ahuja

et al. 2023

Microscopy Res & Technique

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Diabetic retinopathy (DR) is a prevalent cause of global visual impairment, contributing to approximately 4.8% of blindness cases worldwide as reported by the World Health Organization (WHO). The condition is characterized by pathological abnormalities in the retinal layer, including microaneurysms, vitreous hemorrhages, and exudates. Microscopic analysis of retinal images is crucial in diagnosing and treating DR. This article proposes a novel method for early DR screening using segmentation and unsupervised learning techniques. The approach integrates a neural network energy‐based model into the Fuzzy C‐Means (FCM) algorithm to enhance convergence criteria, aiming to improve the accuracy and efficiency of automated DR screening tools. The evaluation of results includes the primary dataset from the Shiva Netralaya Centre, IDRiD, and DIARETDB1. The performance of the proposed method is compared against FCM, EFCM, FLICM, and M‐FLICM techniques, utilizing metrics such as accuracy in noiseless and noisy conditions and average execution time. The results showcase auspicious performance on both primary and secondary datasets, achieving accuracy rates of 99.03% in noiseless conditions and 93.13% in noisy images, with an average execution time of 16.1 s. The proposed method holds significant potential in medical image analysis and could pave the way for future advancements in automated DR diagnosis and management.Research Highlights A novel approach is proposed in the article, integrating a neural network energy‐based model into the FCM algorithm to enhance the convergence criteria and the accuracy of automated DR screening tools. By leveraging the microscopic characteristics of retinal images, the proposed method significantly improves the accuracy of lesion segmentation, facilitating early detection and monitoring of DR. The evaluation of the method's performance includes primary datasets from reputable sources such as the Shiva Netralaya Centre, IDRiD, and DIARETDB1, demonstrating its effectiveness in comparison to other techniques (FCM, EFCM, FLICM, and M‐FLICM) in terms of accuracy in both noiseless and noisy conditions. It achieves impressive accuracy rates of 99.03% in noiseless conditions and 93.13% in noisy images, with an average execution time of 16.1 s.

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Energy based Methods for Medical Image Segmentation

Cited by 13 publications

References 32 publications

Multi‐feature data mining for CT image recognition

Multi‐feature data mining for CT image recognition

Fast Segmentation Methods for Medical Images

Micro‐segmentation of retinal image lesions in diabetic retinopathy using energy‐based fuzzy C‐Means clustering (EFM‐FCM)

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