98,709 research outputs found
Digital Image Analysis of Vitiligo for Monitoring of Vitiligo Treatment
Vitiligo is an acquired pigmentary skin disorder characterized by depigmented macules
that result from damage to and destruction of epidermal melanocytes. Visually, the
vitiligous areas are paler in contrast to normal skin or completely white due to the lack of
pigment melanin. The course of vitiligo is unpredictable where the vitiligous skin lesions
may remain stable for years before worsening.
Vitiligo treatments have two objectives, to arrest disease progression and to re-pigment
the vitiligous skin lesions. To monitor the efficacy of the treatment, dermatologists
observe the disease directly, or indirectly using digital photos. Currently there is no
objective method to determine the efficacy of the vitiligo treatment. Physician's Global
Assessment (PGA) scale is the current scoring system used by dermatologists to evaluate
the treatment. The scale is based on the degree of repigmentation within lesions over
time. This quantitative tool however may not be help to detect slight changes due to
treatment as it would still be largely dependent on the human eye and judgment to
produce the scorings. In addition, PGA score is also subjective, as it varies with
dermatologists.
The progression of vitiligo treatment can be very slow and can take more than 6 months.
It is observed that dermatologists find it visually hard to determine the areas of skin
repigmentation due to this slow progress and as a result the observations are made after a
longer time frame. The objective of this research is to develop a tool that enables
dermatologists to determine and quantify areas of repigmentation objectively over a
shorter time frame during treatment. The approaches towards achieving this objective are
based on digital image processing techniques.
Skin color is due to the combination of skin histological parameters, namely pigment
melanin and haemoglobin. However in digital imaging, color is produced by combining three different spectral bands, namely red, green, and blue (RGB). It is believed that the
spatial distribution of melanin and haemoglobin in skin image could be separated.
It is found that skin color distribution lies on a two-dimensional melanin-haemoglobin
color subspace. In order to determine repigmentation (due to pigment melanin) it is
necessary to perform a conversion from RGB skin image to this two-dimensional color
subspace. Using principal component analysis (PCA) as a dimensional reduction tool,
the two-dimensional subspace can be represented by its first and second principal
components. Independent component analysis is employed to convert the twodimensional
subspace into a skin image that represents skin areas due to melanin and
haemoglobin only.
In the skin image that represents skin areas due to melanin, vitiligous skin lesions are
identified as skin areas that lack melanin. Segmentation is performed to separate the
healthy skin and the vitiligous lesions. The difference in the vitiligous surface areas
between skin images before and after treatment will be expressed as a percentage of
repigmentation in each vitiligo lesion. This percentage will represent the repigmentation
progression of a particular body region.
Results of preliminary and pre-clinical trial study show that our vitiligo monitoring
system has been able to determine repigmentation progression objectively and thus
treatment efficacy on a shorter time cycle. An intensive clinical trial is currently
undertaken in Hospital Kuala Lumpur using our developed system.
VI
Face recognition using color local binary pattern from mutually independent color channels
In this paper, a high performance face recognition system based on local
binary pattern (LBP) using the probability distribution functions (PDF) of
pixels in different mutually independent color channels which are robust to
frontal homogenous illumination and planer rotation is proposed. The
illumination of faces is enhanced by using the state-of-the-art technique which
is using discrete wavelet transform (DWT) and singular value decomposition
(SVD). After equalization, face images are segmented by use of local Successive
Mean Quantization Transform (SMQT) followed by skin color based face detection
system. Kullback-Leibler Distance (KLD) between the concatenated PDFs of a
given face obtained by LBP and the concatenated PDFs of each face in the
database is used as a metric in the recognition process. Various decision
fusion techniques have been used in order to improve the recognition rate. The
proposed system has been tested on the FERET, HP, and Bosphorus face databases.
The proposed system is compared with conventional and thestate-of-the-art
techniques. The recognition rates obtained using FVF approach for FERET
database is 99.78% compared with 79.60% and 68.80% for conventional gray scale
LBP and Principle Component Analysis (PCA) based face recognition techniques
respectively.Comment: 11 pages in EURASIP Journal on Image and Video Processing, 201
Review of Face Detection Systems Based Artificial Neural Networks Algorithms
Face detection is one of the most relevant applications of image processing
and biometric systems. Artificial neural networks (ANN) have been used in the
field of image processing and pattern recognition. There is lack of literature
surveys which give overview about the studies and researches related to the
using of ANN in face detection. Therefore, this research includes a general
review of face detection studies and systems which based on different ANN
approaches and algorithms. The strengths and limitations of these literature
studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa
Simultaneous Localization and Recognition of Dynamic Hand Gestures
A framework for the simultaneous localization and recognition of dynamic hand gestures is proposed. At the core of this framework is a dynamic space-time warping (DSTW) algorithm, that aligns a pair of query and model gestures in both space and time. For every frame of the query sequence, feature detectors generate multiple hand region candidates. Dynamic programming is then used to compute both a global matching cost, which is used to recognize the query gesture, and a warping path, which aligns the query and model sequences in time, and also finds the best hand candidate region in every query frame. The proposed framework includes translation invariant recognition of gestures, a desirable property for many HCI systems. The performance of the approach is evaluated on a dataset of hand signed digits gestured by people wearing short sleeve shirts, in front of a background containing other non-hand skin-colored objects. The algorithm simultaneously localizes the gesturing hand and recognizes the hand-signed digit. Although DSTW is illustrated in a gesture recognition setting, the proposed algorithm is a general method for matching time series, that allows for multiple candidate feature vectors to be extracted at each time step.National Science Foundation (CNS-0202067, IIS-0308213, IIS-0329009); Office of Naval Research (N00014-03-1-0108
Detección y segmentación de Eritema en lesiones de la piel basado en imágenes dermatoscópicas
El eritema es un tipo de lesión cutánea que se presenta como un enrojecimiento de la piel y suele estar asociado
a una inflamación de la piel. La presencia de Eritema en otro tipo de lesiones o enfermedades es muy frecuente.
Cuantificar el eritema permite al dermatólogo dar un correcto diagnóstico, ya que en ocasiones el eritema es el
primer y único síntoma de algunas enfermedades infecciosas cutáneas.
En este proyecto, empleando imágenes dermatoscópicas de lesiones de la piel, trataremos de clasificar las áreas
de la lesión en Eritema, Piel Pigmentada y Piel Nomal. Para ello, nos basaremos en los primeros pasos del
algoritmo descrito por Kharazmi et al. [1] para la segmentación de estructuras vasculares. Primero aplicamos un
proceso de descomposición del color de la piel, para ello se utiliza el Analisis de Componentes Principales, el
Análisis de Componentes Independientes e información del canal a* del espacio de color CIE L*a*b*. Con esto
obtendremos las componentes de melanina y hemoglobina. A continuación, utilizamos un clasificador basado
en la distancia de Mahalanobis sobre la componente de hemoglobina para clasificar los pixeles de la imagen en
3 clasificadores: Piel Normal, Piel Pigmentada y Eritema. Como resultado obtendremos la segmentación de las
tres áreas de interés.Erythema is a type of skin lesion that appears as a skin redness and it is usually associated with skin
inflammation. The presence of Erythema in other types of lesions or diseases is very frequent. Quantifying
erythema allows the dermatologist to make a correct diagnosis, since erythema in some cases is the first and
only symptom of some infectious skin diseases.
In this project, using dermoscopic images of skin lesions, we will try to classify the areas of the lesion into
Erythema, Pigmented Skin, and Nomal Skin. For this, we will base on the first steps of the algorithm described
by Kharazmi et al. [1] for the segmentation of vascular structures. First, we apply a skin color decomposition
process, using the Principal Component Analysis, the Independent Component Analysis and information from
the a * channel of the CIE L * a * b * color space. With this we will obtain the components of melanin and
hemoglobin. Next, we use a classifier based on the Mahalanobis distance on the hemoglobin component to
classify the pixels of the image into 3 classifiers: Normal Skin, Pigmented Skin and Erythema. As a result we
will have the segmentation of the three areas of interest.Universidad de Sevilla. Grado en Ingeniería de las Tecnologías de Telecomunicació
Analysis of the contour structural irregularity of skin lesions using wavelet decomposition
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
- …