5 research outputs found
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Detection of melanoma skin cancer in dermoscopy images
Malignant melanoma is the most hazardous type of human skin cancer and its incidence has been rapidly increasing. Early detection of malignant melanoma in dermoscopy images is very important and critical, since its detection in the early stage can be helpful to cure it. Computer Aided Diagnosis systems can be very helpful to facilitate the early detection of cancers for dermatologists. In this paper, we present a novel method for the detection of melanoma skin cancer. To detect the hair and several noise from images, preprocessing step is carried out by applying a bank of directional lters. and therefore, Image inpainting method is implemented to ll in the unknown regions. Fuzzy C-Means and Markov Random Field methods are used to delineate the border of the lesion area in the images. The method was evaluated on a dataset of 200 dermoscopic images, and superior results were produced compared to alternative methods
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Detection of pigment network in dermoscopy images
One of the most important structures in dermoscopy images is the pigment network, which is also one of the most challenging and fundamental task for dermatologists in early detection of melanoma. This paper presents an automatic system to detect pigment network from dermoscopy images. The design of the proposed algorithm consists of four stages. First, a pre-processing algorithm is carried out in order to remove the noise and improve the quality of the image. Second, a bank of directional filters and morphological connected component analysis are applied to detect the pigment networks. Third, features are extracted from the detected image, which can be used in the subsequent stage. Fourth, the classification process is performed by applying feed-forward neural network, in order to classify the region as either normal or abnormal skin. The method was tested on a dataset of 200 dermoscopy images from Hospital Pedro Hispano (Matosinhos), and better results were produced compared to previous studies
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Lesion segmentation in dermoscopy images using particle swarm optimization and markov random field
Malignant melanoma is one of the most rapidly
increasing cancers globally and it is the most dangerous
form of human skin cancer. Dermoscopy is one of the major
imaging modalities used in the diagnosis of melanoma. Early
detection of melanoma can be helpful and usually curable.
Due to the difficulty for dermatologists in the interpretation of
dermoscopy images, Computer Aided Diagnosis systems can
be very helpful to facilitate the early detection. The automated
detection of the lesion borders is one of the most important
steps in dermoscopic image analysis. In this paper, we present a
fully automated method for melanoma border detection using
image processing techniques. The hair and several noises are
detected and removed by applying a bank of directional filters
and Image Inpainting method respectively. A hybrid method
is developed by combining Particle Swarm Optimization and
Markov Random Field methods, in order to delineate the
border of the lesion area in the images. The method was tested
on a dataset of 200 dermoscopic images, and the experimental
results show that our method is superior in terms of the
accuracy of drawing the lesion borders compared to alternative
methods
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Automatic Annotation of Retinal Layers in Optical Coherence Tomography Images
Early diagnosis of retinal OCT images has been shown to curtail blindness and visual impairments. However, the advancement of ophthalmic imaging technologies produces an ever-growing scale of retina images, both in volume and variety, which overwhelms the ophthalmologist ability to segment these images. While many automated methods exist, speckle noise and intensity inhomogeneity negatively impacts the performance of these methods. We present a comprehensive and fully automatic method for annotation of retinal layers in OCT images comprising of fuzzy histogram hyperbolisation (FHH) and graph cut methods to segment 7 retinal layers across 8 boundaries. The FHH handles speckle noise and inhomogeneity in the preprocessing step. Then the normalised vertical image gradient, and it’s inverse to represent image intensity in calculating two adjacency matrices and then the FHH reassigns the edge-weights to make edges along retinal boundaries have a low cost, and graph cut method identifies the shortest-paths (layer boundaries). The method is evaluated on 150 B-Scan images, 50 each from the temporal, foveal and nasal regions were used in our study. Promising experimental results have been achieved with high tolerance and adaptability to contour variance and pathological inconsistency of the retinal layers in all (temporal, foveal and nasal) regions. The method also achieves high accuracy, sensitivity, and Dice score of 0.98360, 0.9692 and 0.9712, respectively in segmenting the retinal nerve fibre layer. The annotation can facilitate eye examination by providing accurate results. The integration of the vertical gradients into the graph cut framework, which captures the unique characteristics of retinal structures, is particularly useful in finding the actual minimum paths across multiple retinal layer boundaries. Prior knowledge plays an integral role in image segmentation
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Skin Cancer Detection in Dermoscopy Images Using Sub-Region Features
Abstract. In the medical field, the identification of skin cancer (Malignant
Melanoma) in dermoscopy images is still a challenging task for
radiologists and researchers. Due to its rapid increase, the need for decision
support systems to assist the radiologists to detect it in early stages
becomes essential and necessary. Computer Aided Diagnosis (CAD) systems
have significant potential to increase the accuracy of its early detection.
Typically, CAD systems use various types of features to characterize
skin lesions. The features are often concatenated into one vector (early
fusion) to represent the image. In this paper, we present a novel method
for melanoma detection from images. First the lesions are segmented
by combining Particle Swarm Optimization and Markov Random Field
methods. Then the K-means is applied on the segmented lesions to separate
them into homogeneous clusters, from which important features are
extracted. Finally, an Artificial Neural Network with Radial Basis Function
is applied for the detection of melanoma. The method was tested
on 200 dermoscopy images. The experimental results show that the proposed
method achieved higher accuracy in terms of melanoma detection,
compared to alternative methods