Optimization neural networks for the segmentation of magnetic resonance images

Amartur, Sundar; Piraino, David W.; Takefuji, Yoshiyasu

doi:10.1109/42.141645

Cited by 146 publications

(53 citation statements)

References 14 publications

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“…T 1 -weighted, T 2 -weighted and proton density data) since this offers a greater potential for discriminating between different tissues (e.g., [16,17]). In the present work, only T 1 -weighted images were obtained, because of the prohibitive cost of collecting multi-modal data for a research project.…”

Section: Image Data and Labellingmentioning

confidence: 99%

Segmentation of magnetic resonance images using a combination of neural networks and active contour models

Middleton

Damper

2004

Medical Engineering & Physics

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Segmentation of medical images is very important for clinical research and diagnosis, leading to a requirement for robust automatic methods. This paper reports on the combined use of a neural network (a multilayer perceptron, MLP) and active contour model ('snake') to segment structures in magnetic resonance (MR) images. The perceptron is trained to produce a binary classification of each pixel as either a boundary or a non-boundary point. Subsequently, the resulting binary (edge-point) image forms the external energy function for a snake, used to link the candidate boundary points into a continuous, closed contour. We report here on the segmentation of the lungs from multiple MR slices of the torso; lung-specific constraints have been avoided to keep the technique as general as possible. In initial investigations, the inputs to the MLP were limited to normalised intensity values of the pixels from an (7 × 7) window scanned across the image. The use of spatial coordinates as additional inputs to the MLP is then shown to provide an improvement in segmentation performance as quantified using the effectiveness measure (a weighted product of precision and recall). Training sets were first developed using a lengthy iterative process. Thereafter, a novel cost function based on effectiveness is proposed for training that allows us to achieve dramatic improvements in segmentation performance, as well as faster, non-iterative selection of training examples. The classifications produced using this cost function were sufficiently good that the binary image produced by the MLP could be post-processed using an active contour model to provide an accurate segmentation of the lungs from the multiple slices in almost all cases, including unseen slices and subjects.

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Section: Image Data and Labellingmentioning

confidence: 99%

Segmentation of magnetic resonance images using a combination of neural networks and active contour models

Middleton

Damper

2004

Medical Engineering & Physics

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“…Ozkan et al 14 present preliminary results of a computer system for automatic multispectral MRI analysis. Amarkur et al 15 present another neural net approach to solving the problem, based on the Hopfield network. MARA (Multi-layer Adaptive Resonance Architecture), 16 which uses a stable and plastic self-organizing neural network, is capable of recognizing, reconstructing, and segmenting the traces of previously learned binary patterns.…”

Section: Related Reportsmentioning

confidence: 99%

“…The next step is the selection of the statistical analysis space (SAS) for use within the segmentation model. Various SASs have been used, the image itself, 15,22 gray-level histograms, 21,28,29 co-occurrence maps, 30,31 and multi-spectral images. 14,32 Because both region and boundary information are required for proper segmentation, a method that combines these two features is desirable.…”

Section: General Model Descriptionmentioning

confidence: 99%

Multivariate Statistical Model for 3D Image Segmentation with Application to Medical Images

2003

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In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probabilitybased multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).

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“…The second stage is segmentation (Armatur et al, 1992) of extracted lung region using Fuzzy Possibilistic C Mean (FPCM) algorithm. Then the diagnosis rules for detecting false positive regions are elaborated.…”

Section: Methodsmentioning

confidence: 99%

A Computer Aided Diagnosis System for Lung Cancer Detection \Using Support Vector Machine

Gomathi¹,

Thangaraj²

2010

American Journal of Applied Sciences

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Problem statement: Computer Tomography (CT) has been considered as the most sensitive imaging technique for early detection of lung cancer. Approach: On the other hand, there is a requirement for automated methodology to make use of large amount of data obtained CT images. Computer Aided Diagnosis (CAD) can be used efficiently for early detection of Lung Cancer. Results: The usage of existing CAD system for early detection of lung cancer with the help of CT images has been unsatisfactory because of its low sensitivity and False Positive Rates (FPR). This study presents a CAD system which can automatically detect the lung cancer nodules with reduction in false positive rates. In this study, different image processing techniques are applied initially in order to obtain the lung region from the CT scan chest images. Then the segmentation is carried with the help of Fuzzy Possibility C Mean (FPCM) clustering algorithm. Conclusion/Recommendations: Finally for automatic detection of cancer nodules, Support Vector Machine (SVM) is used which helps in better classification of cancer nodules. The experimentation is conducted for the proposed technique by 1000 CT images collected from the reputed hospital.

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Optimization neural networks for the segmentation of magnetic resonance images

Cited by 146 publications

References 14 publications

Segmentation of magnetic resonance images using a combination of neural networks and active contour models

Segmentation of magnetic resonance images using a combination of neural networks and active contour models

Multivariate Statistical Model for 3D Image Segmentation with Application to Medical Images

A Computer Aided Diagnosis System for Lung Cancer Detection \Using Support Vector Machine

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