Segmentation of breast MR images using a generalised 2D mathematical model with inflation and deflation forces of active contours

Rampun, Andrik; Scotney, Bryan; Morrow, Philip; Wang, Hui; Winder, John

doi:10.1016/j.artmed.2018.10.007

Cited by 18 publications

(5 citation statements)

References 38 publications

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“…After normalization, the super-pixel similarity of computed tomography sequence image features will dominate the similarity measure, which weakens the low-dimensional similarity characteristics of super-pixels. Make different features have different weights m to balance the proportion of different features in the similarity calculation [ 25 , 26 ] and reduce the proportion of irrelevant features G H : …”

Section: Methodsmentioning

confidence: 99%

Optimization algorithm of CT image edge segmentation using improved convolution neural network

Wang

Wei

2022

PLoS ONE

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To address the problem of high failure rate and low accuracy in computed tomography (CT) image edge segmentation, we proposed a CT sequence image edge segmentation optimization algorithm using improved convolution neural network. Firstly, the pattern clustering algorithm is applied to cluster the pixels with relationship in the CT sequence image space to extract the edge information of the real CT image; secondly, Euclidean distance is used to calculate similarity and measure similarity, according to the measurement results, convolution neural network (CNN) hierarchical optimization is carried out to improve the convergence ability of CNN; finally, the pixel classification of CT sequence images is carried out, and the edge segmentation of CT sequence images is optimized according to the classification results. The results show that the overall recognition rate of this method is at a high level. The training time is obviously reduced when the training times exceed 12 times, the recall rate is always about 90%, and the accuracy of image segmentation is high, which solves the problem of large failure rate and low accuracy.

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Section: Methodsmentioning

confidence: 99%

Optimization algorithm of CT image edge segmentation using improved convolution neural network

Wang

Wei

2022

PLoS ONE

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“…Initially, the image undergoes enhancement procedures, typically employing morphological operations [33], matched filter responses [39], the complex continuous wavelet transform [37], adaptive histogram equalization [35], Hessian-based filters [36,38,44], among others. Sub-sequently, segmentation occurs through multilevel thresholding [40][41][42][43]48] or region-oriented techniques such as region growing [45,49] or active contours [46,47]. These conventional unsupervised methods heavily rely on manual feature extraction for image element representation and segmentation.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2024

SIPIJ

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The structure of retinal blood vessels is crucial for the early detection of diabetic retinopathy, a leading cause of blindness worldwide. Yet, accurately segmenting retinal vessels poses significant challenges due to the low contrast and noise present in capillaries.The automated segmentation of retinal blood vessels significantly enhances Computer-Aided Diagnosis for diverse ophthalmic and cardiovascular conditions. It is imperative to develop a method capable of segmenting both thin and thick retinal vessels to facilitate medical analysis and disease diagnosis effectively. This article introduces a novel methodology for robust vessel segmentation, addressing prevalent challenges identified in existing literature.The methodology PSO-HRVSO comprises three key stages: pre-processing, main processing, and postprocessing. In the initial stage, filters are employed for image smoothing and enhancement, leveraging PSO optimization. The main processing phase is bifurcated into two configurations. Initially, thick vessels are segmented utilizing an optimized top-hat approach, homo-morphic filtering, and median filter. Subsequently, the second configuration targets thin vessel segmentation, employing the optimized top-hat method, homomorphic filtering, and matched filter. Lastly, morphological image operations are conducted during the post-processing stage.The PSO-HRVSO method underwent evaluation using two publicly accessible databases (DRIVE and STARE), measuring performance across three key metrics: specificity, sensitivity, and accuracy. Analysis of the outcomes revealed averages of 0.9891, 0.8577, and 0.0.9852 for the DRIVE dataset, and 0.9868, 0.8576, and 0.9831 for the STARE dataset, respectively.The PSO-HRVSO technique yields numerical results that demonstrate competitive average values when compared to current methods. Moreover, it sur-passes all leading unsupervised methods in terms of specificity and accuracy. Additionally, it outperforms the majority of state-of-the-art supervised methods without incurring the computational costs associated with such algorithms. Detailed visual analysis reveals that the PSO-HRVSO approach enables a more precise segmentation of thin vessels compared to alternative procedures.

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“…The objective is to develop a web-based software ecosystem dedicated to the personalized, collaborative, and multidisciplinary management of primary breast cancer, from diagnosis, to therapy, and follow-up. The DESIREE platform offers some image-based diagnostic decision support modalities involving mammogram-based breast density classification [26], fully automated breast boundary and pectoral muscle segmentation [27], and breast mass classification using ensemble convolutional neural networks [28]. Research works on predictive modeling have also been conducted, e.g., to predict the esthetic outcome of Breast Conservative Therapy considering mechanical forces due to gravity, breast density and tissue distribution, and the inflammation induced by radiotherapy and the wound healing [29].…”

Section: Introductionmentioning

confidence: 99%

Implementation of an ontological reasoning to support the guideline-based management of primary breast cancer patients in the DESIREE project

Bouaud

Pelayo

Lamy³

et al. 2020

Artificial Intelligence in Medicine

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The DESIREE project has developed a platform offering several complementary therapeutic decision support modules to improve the quality of care for breast cancer patients. All modules are operating consistently with a common breast cancer knowledge model (BCKM) following the generic entity-attribute-value model. The BCKM is formalized as an ontology including both the data model to represent clinical patient information and the termino-ontological model to represent the application domain concepts. This ontological model is used to describe data semantics and to allow for reasoning at different levels of abstraction. We present the guidelinebased decision support module (GL-DSS). Three breast cancer clinical practice guidelines have been formalized as decision rules including evidence levels, conformance levels, and two types of dependency, "refinement" and "complement", used to build complete care plans from the reconciliation of atomic recommendations. The system has been assessed on 138 decisions previously made without the system and re-played with the system after a washout period on simulated tumor boards (TBs) in three pilot sites. When TB clinicians changed their decision after using the GL-DSS, it was for a better decision than the decision made without the system in 75 % of the cases.(consolidated figures in 2018), breast cancer mortality is declining in France and breast cancer is one of the best prognosis cancers with among the best five and ten-year survival rates (87 % and 76 %, respectively). However, it still remains a therapeutic challenge especially for triple negative breast cancers and HER2+ breast cancers, for which improvements are both possible and necessary [1].Clinical practice guidelines (CPGs) are free-text documents developed by National agencies or academic associations to provide the best recommendations for the management of a set of selected patient profiles. These recommendations are built from published clinical research results and represent the state of the art following evidencebased medicine principles [2,3]. Although studies have shown that implementing oncology CPGs does improve clinical outcomes in both overall and recurrence-free survivals of cancer patients [4][5][6][7][8][9][10][11], there

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Segmentation of breast MR images using a generalised 2D mathematical model with inflation and deflation forces of active contours

Cited by 18 publications

References 38 publications

Optimization algorithm of CT image edge segmentation using improved convolution neural network

Optimization algorithm of CT image edge segmentation using improved convolution neural network

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Implementation of an ontological reasoning to support the guideline-based management of primary breast cancer patients in the DESIREE project

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