Malignant and Benign Mass Segmentation in Mammograms Using Active Contour Methods

Ciecholewski, Marcin

doi:10.3390/sym9110277

Cited by 20 publications

(14 citation statements)

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“…The number of ROIs that were experimentally tested was 600 (300 benign and 300 malignant) and 40% of them were used for training while 60% for testing. Active contour (AC) methods were employed in [42] to define the borders of breast masses. The parameters of the AC models were tuned using a training set of 20 ROIs (10 benign and 10 malignant masses of various shapes, sizes, margins and brightness).…”

Section: Experimental Results Presented In the Referenced Approachesmentioning

confidence: 99%

“…Wavelets are isotropic objects that cannot be efficiently used in image filtering applications. Another type of anisotropic filtering is the anisotropic diffusion filtering (ADF) employed in [42] for breast mass segmentation. ADF is an iterative filter that reduces the noise in the image while preserving the region borders.…”

Section: Image Enhancement Filtering Methodsmentioning

confidence: 99%

“…In this paper, genetic algorithms detect dissimilar regions between the left and right brain hemispheres. Both the active contour and ACWE methods have been employed in [42] for malignant and benign mass segmentation in mammograms. Active contour segmentation techniques have also been employed in [16] for skin melanoma diagnosis.…”

Section: Segmentation Methodsmentioning

confidence: 99%

“…Most of the approaches described in this section are supervised learning techniques. Typical unsupervised learning includes the expectation-maximization algorithm used in [42], some special types of neural networks like deep belief nets [41], etc.…”

Section: Classification Methodsmentioning

confidence: 99%

“…Mammograms are images used to detect breast cancer [41][42][43][44][45]. The ordinary color space of these image processing techniques is RGB.…”

Section: Introductionmentioning

confidence: 99%

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A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis

Petrellis

2018

Symmetry

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Image processing has been extensively used in various (human, animal, plant) disease diagnosis approaches, assisting experts to select the right treatment. It has been applied to both images captured from cameras of visible light and from equipment that captures information in invisible wavelengths (magnetic/ultrasonic sensors, microscopes, etc.). In most of the referenced diagnosis applications, the image is enhanced by various filtering methods and segmentation follows isolating the regions of interest. Classification of the input image is performed at the final stage. The disease diagnosis approaches based on these steps and the common methods are described. The features extracted from a plant/skin disease diagnosis framework developed by the author are used here to demonstrate various techniques adopted in the literature. The various metrics along with the available experimental conditions and results presented in the referenced approaches are also discussed. The accuracy achieved in the diagnosis methods that are based on image processing is often higher than 90%. The motivation for this review is to highlight the most common and efficient methods that have been employed in various disease diagnosis approaches and suggest how they can be used in similar or different applications.

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Section: Experimental Results Presented In the Referenced Approachesmentioning

confidence: 99%

Section: Image Enhancement Filtering Methodsmentioning

confidence: 99%