A narrow band graph partitioning method for skin lesion segmentation

Yuan, Xiaojing; Situ, Ning; Zouridakis, George

doi:10.1016/j.patcog.2008.09.006

Cited by 68 publications

(37 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is an abundance of literature that details a wide range of image segmentation approaches and methodologies including, but not limited to, methods employing histogram thresholding [10,11], clustering [12,13], active contours [14,15], edge detection [16,17], graph theory [18], and probabilistic modeling [19,20]. Successful application of these methods, either individually or in combination, is expected to achieve optimum segmentation accuracy while maintaining the robustness to noise [21,22].…”

Section: Related Workmentioning

confidence: 99%

An SVM Framework for Malignant Melanoma Detection Based on Optimized HOG Features

Bakheet

2017

Computation

View full text Add to dashboard Cite

Early detection of skin cancer through improved techniques and innovative technologies has the greatest potential for significantly reducing both morbidity and mortality associated with this disease. In this paper, an effective framework of a CAD (Computer-Aided Diagnosis) system for melanoma skin cancer is developed mainly by application of an SVM (Support Vector Machine) model on an optimized set of HOG (Histogram of Oriented Gradient) based descriptors of skin lesions. Experimental results obtained by applying the presented methodology on a large, publicly accessible dataset of dermoscopy images demonstrate that the proposed framework is a strong contender for the state-of-the-art alternatives by achieving high levels of sensitivity, specificity, and accuracy (98.21%, 96.43% and 97.32%, respectively), without sacrificing computational soundness.

show abstract

Section: Related Workmentioning

confidence: 99%

An SVM Framework for Malignant Melanoma Detection Based on Optimized HOG Features

Bakheet

2017

Computation

View full text Add to dashboard Cite

show abstract

“…Some of them are clustering [28], histogram-based methods [29], watershed transformation methods [30], graph partitioning [31] and image pyramids methods [32]. Some of the practical applications of image segmentation are medical imaging [28], objects classification and face recognition [33].…”

Section: S Egmenting Digital Im Ages In Constant Timementioning

confidence: 99%

A bio-inspired software for segmenting digital images

Dfaz-Pernil

Molina‐Abril

Real

et al. 2010

2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)

View full text Add to dashboard Cite

Abstract-Segmentation in computer vision refers to the pro cess of partitioning a digital image into multiple segments (sets of pixels). It has several features which make it suitable for techniques inspired by nature. It can be parallelized , locally solved and the input data can be easily encoded by bio inspired representations. In this paper, we present a new software for performing a segmentation of 2D digital images based on Membrane Computing techniques. I. I NTRODUCTIONNature is a big inspiration source for designing solutions to a broad panoply of problems. Natural Computing studies com putational paradigms inspired from various well known natural phenomena in physics, chemistry and biology! . It abstracts the way in which nature acts, conceiving new computing models. All these computational paradigms have in common the use of an alternative way of encoding the information, adapted to the bio-inspired substrate and the use of intrinsic parallelism of natural processes.In this paper we present a bio-inspired software for solving the Segmentation Problem in Digital Imagery. Segmentation in computer vision (see [11]), refers to the process of partitioning 978-1-4244-6439-5/10/$26.00 ©2010 IEEE Spain magutier@us.es a digital image into multiple segments (sets of pixels). The goal of segmentation is to simplify and/or change the repre sentation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics.Segmentation in Digital Imagery has several features which make it suitable for techniques inspired by nature. One of them is that it can be parallelized and locally solved. Regardless how large is the picture, the segmentation process can be performed in parallel in different local areas of it. Another interesting feature is that the basic necessary information can be easily encoded by bio-inspired representations.In the literature, one can find several attempts for bridging problems from Digital Imagery with Natural Computing as the works by K.G. Subramanian et al.[12], [13] or the work by Chao and Nakayama where Natural Computing and Algebraic Topology are linked by using Neural Networks [14] (extended Kohonen mapping). In this paper, we will use an information encoding and techniques borrowed from Membrane Computing.Membrane Computing is a theoretical model of compu tation inspired by the structure and functioning of cells as living organisms able to process and generate information. The computational devices are called P systems [9]. Roughly speaking, a P system consists of a membrane structure, in the compartments of which one places multi sets of objects which evolve according to given rules. In the most extended model, the rules are applied in a synchronous non-deterministic max imally parallel manner, but some other s...

show abstract

“…In the previous decade, there have been many studies concerning the boundary irregularity of lesions using both geometric [7,8,9,10] and local fractal [11] measures. There are two types of irregularities found on boundaries: textual and structural, these correspond to fine changes and obvious convex and concave features respectively [7].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the contour structural irregularity of skin lesions using wavelet decomposition

Staunton

2013

Pattern Recognition

View full text Add to dashboard Cite

The boundary irregularity of skin lesions is of clinical significance for the early detection of malignant melanomas and to distinguish them from other lesions such as benign moles. The structural components of the contour are of particular importance. To extract the structure from the contour, wavelet decomposition was used as these components tend to locate in the lower frequency sub-bands. Lesion contours were modeled as signatures with scale normalization to give position and frequency resolution invariance. Energy distributions among different wavelet sub-bands were then analyzed to extract those with significant levels and differences to enable maximum discrimination.Based on the coefficients in the significant sub-bands, structural components from the original contours were modeled, and a set of statistical and geometric irregularity descriptors researched that were applied at each of the significant sub-bands. The effectiveness of the descriptors was measured using the Hausdorff distance between sets of data from melanoma and mole contours.The best descriptor outputs were input to a back projection neural network to construct a combined classifier system. Experimental results showed that thirteen features from four sub-bands produced the best discrimination between sets of melanomas and moles, and that a small training set of nine melanomas and nine moles was optimum.

show abstract

A narrow band graph partitioning method for skin lesion segmentation

Cited by 68 publications

References 30 publications

An SVM Framework for Malignant Melanoma Detection Based on Optimized HOG Features

An SVM Framework for Malignant Melanoma Detection Based on Optimized HOG Features

A bio-inspired software for segmenting digital images

Analysis of the contour structural irregularity of skin lesions using wavelet decomposition

Contact Info

Product

Resources

About