Abstract: Image segmentation plays an important role in
medical imaging applications. Therefore, accurate methods are
needed for the successful segmentation of medical images for
diagnosis and detection of various diseases. In this paper, we have
used maximum entropy to achieve image segmentation. Maximum
entropy has been calculated using Shannon, Renyi and Tsallis
entropies. This work has novelty based on the detection of skin lesion
caused by the bite of a parasite called Sand Fly causing the disease is
called Cutaneous Leishmaniasis.
Abstract: This paper proposes a rotational invariant texture
feature based on the roughness property of the image for psoriasis
image analysis. In this work, we have applied this feature for image
classification and segmentation. The fuzzy concept is employed to
overcome the imprecision of roughness. Since the psoriasis lesion is
modeled by a rough surface, the feature is extended for calculating
the Psoriasis Area Severity Index value. For classification and
segmentation, the Nearest Neighbor algorithm is applied. We have
obtained promising results for identifying affected lesions by using
the roughness index and severity level estimation.
Abstract: In medical imaging, segmentation of different areas of
human body like bones, organs, tissues, etc. is an important issue.
Image segmentation allows isolating the object of interest for further
processing that can lead for example to 3D model reconstruction of
whole organs. Difficulty of this procedure varies from trivial for
bones to quite difficult for organs like liver. The liver is being
considered as one of the most difficult human body organ to segment.
It is mainly for its complexity, shape versatility and proximity of
other organs and tissues. Due to this facts usually substantial user
effort has to be applied to obtain satisfactory results of the image
segmentation. Process of image segmentation then deteriorates from
automatic or semi-automatic to fairly manual one. In this paper,
overview of selected available software applications that can handle
semi-automatic image segmentation with further 3D volume
reconstruction of human liver is presented. The applications are being
evaluated based on the segmentation results of several consecutive
DICOM images covering the abdominal area of the human body.
Abstract: Segmentation is one of the essential tasks in image
processing. Thresholding is one of the simplest techniques for
performing image segmentation. Multilevel thresholding is a simple
and effective technique. The primary objective of bi-level or
multilevel thresholding for image segmentation is to determine a best
thresholding value. To achieve multilevel thresholding various
techniques has been proposed. A study of some nature inspired
metaheuristic algorithms for multilevel thresholding for image
segmentation is conducted. Here, we study about Particle swarm
optimization (PSO) algorithm, artificial bee colony optimization
(ABC), Ant colony optimization (ACO) algorithm and Cuckoo
search (CS) algorithm.
Abstract: Color Histogram is considered as the oldest method
used by CBIR systems for indexing images. In turn, the global
histograms do not include the spatial information; this is why the
other techniques coming later have attempted to encounter this
limitation by involving the segmentation task as a preprocessing step.
The weak segmentation is employed by the local histograms while
other methods as CCV (Color Coherent Vector) are based on strong
segmentation. The indexation based on local histograms consists of
splitting the image into N overlapping blocks or sub-regions, and
then the histogram of each block is computed. The dissimilarity
between two images is reduced, as consequence, to compute the
distance between the N local histograms of the both images resulting
then in N*N values; generally, the lowest value is taken into account
to rank images, that means that the lowest value is that which helps to
designate which sub-region utilized to index images of the collection
being asked. In this paper, we make under light the local histogram
indexation method in the hope to compare the results obtained against
those given by the global histogram. We address also another
noteworthy issue when Relying on local histograms namely which
value, among N*N values, to trust on when comparing images, in
other words, which sub-region among the N*N sub-regions on which
we base to index images. Based on the results achieved here, it seems
that relying on the local histograms, which needs to pose an extra
overhead on the system by involving another preprocessing step
naming segmentation, does not necessary mean that it produces better
results. In addition to that, we have proposed here some ideas to
select the local histogram on which we rely on to encode the image
rather than relying on the local histogram having lowest distance with
the query histograms.
Abstract: Human face has a fundamental role in the appearance
of individuals. So the importance of facial surgeries is undeniable.
Thus, there is a need for the appropriate and accurate facial skin
segmentation in order to extract different features. Since Fuzzy CMeans
(FCM) clustering algorithm doesn’t work appropriately for
noisy images and outliers, in this paper we exploit Possibilistic CMeans
(PCM) algorithm in order to segment the facial skin. For this
purpose, first, we convert facial images from RGB to YCbCr color
space. To evaluate performance of the proposed algorithm, the
database of Sahand University of Technology, Tabriz, Iran was used.
In order to have a better understanding from the proposed algorithm;
FCM and Expectation-Maximization (EM) algorithms are also used
for facial skin segmentation. The proposed method shows better
results than the other segmentation methods. Results include
misclassification error (0.032) and the region’s area error (0.045) for
the proposed algorithm.
Abstract: Medical image analysis is one of the great effects of computer image processing. There are several processes to analysis the medical images which the segmentation process is one of the challenging and most important step. In this paper the segmentation method proposed in order to segment the dental radiograph images. Thresholding method has been applied to simplify the images and to morphologically open binary image technique performed to eliminate the unnecessary regions on images. Furthermore, horizontal and vertical integral projection techniques used to extract the each individual tooth from radiograph images. Segmentation process has been done by applying the level set method on each extracted images. Nevertheless, the experiments results by 90% accuracy demonstrate that proposed method achieves high accuracy and promising result.
Abstract: Object identification and segmentation application requires extraction of object in foreground from the background. In this paper the Bhattacharya distance based probabilistic approach is utilized with an active contour model (ACM) to segment an object from the background. In the proposed approach, the Bhattacharya histogram is calculated on non-linear structure tensor space. Based on the histogram, new formulation of active contour model is proposed to segment images. The results are tested on both color and gray images from the Berkeley image database. The experimental results show that the proposed model is applicable to both color and gray images as well as both texture images and natural images. Again in comparing to the Bhattacharya based ACM in ICA space, the proposed model is able to segment multiple object too.
Abstract: Breast Cancer is the most common malignancy in women and the second leading cause of death for women all over the world. Earlier the detection of cancer, better the treatment. The diagnosis and treatment of the cancer rely on segmentation of Sonoelastographic images. Texture features has not considered for Sonoelastographic segmentation. Sonoelastographic images of 15 patients containing both benign and malignant tumorsare considered for experimentation.The images are enhanced to remove noise in order to improve contrast and emphasize tumor boundary. It is then decomposed into sub-bands using single level Daubechies wavelets varying from single co-efficient to six coefficients. The Grey Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) features are extracted and then selected by ranking it using Sequential Floating Forward Selection (SFFS) technique from each sub-band. The resultant images undergo K-Means clustering and then few post-processing steps to remove the false spots. The tumor boundary is detected from the segmented image. It is proposed that Local Binary Pattern (LBP) from the vertical coefficients of Daubechies wavelet with two coefficients is best suited for segmentation of Sonoelastographic breast images among the wavelet members using one to six coefficients for decomposition. The results are also quantified with the help of an expert radiologist. The proposed work can be used for further diagnostic process to decide if the segmented tumor is benign or malignant.
Abstract: In this paper, a simple moving human detection method is proposed for video surveillance system or access monitoring system. The frame difference and noise threshold are used for initial detection of a moving human-object, and simple labeling method is applied for final human-object segmentation. The simulated results show that the applied algorithm is fast to detect the moving human-objects by performing 95% of correct detection rate. The proposed algorithm has confirmed that can be used as an intelligent video access monitoring system.
Abstract: Image segmentation is the process to segment a given image into several parts so that each of these parts present in the
image can be further analyzed. There are numerous techniques of image segmentation available in literature. In this paper, authors have been analyzed the edge-based approach for image segmentation. They have been implemented the different edge operators like Prewitt, Sobel, LoG, and Canny on the basis of their threshold parameter. The results of these operators have been shown for
various images.
Abstract: This paper presents a customized deformable model
for the segmentation of abdominal and thoracic aortic aneurysms in
CTA datasets. An important challenge in reliably detecting aortic
aneurysm is the need to overcome problems associated with intensity
inhomogeneities and image noise. Level sets are part of an important
class of methods that utilize partial differential equations (PDEs) and
have been extensively applied in image segmentation. A Gaussian
kernel function in the level set formulation, which extracts the local
intensity information, aids the suppression of noise in the extracted
regions of interest and then guides the motion of the evolving contour
for the detection of weak boundaries. The speed of curve evolution
has been significantly improved with a resulting decrease in
segmentation time compared with previous implementations of level
sets. The results indicate the method is more effective than other
approaches in coping with intensity inhomogeneities.
Abstract: This paper presents an optimal and unsupervised satellite image segmentation approach based on Pearson system and k-Means Clustering Algorithm Initialization. Such method could be considered as original by the fact that it utilised K-Means clustering algorithm for an optimal initialisation of image class number on one hand and it exploited Pearson system for an optimal statistical distributions- affectation of each considered class on the other hand. Satellite image exploitation requires the use of different approaches, especially those founded on the unsupervised statistical segmentation principle. Such approaches necessitate definition of several parameters like image class number, class variables- estimation and generalised mixture distributions. Use of statistical images- attributes assured convincing and promoting results under the condition of having an optimal initialisation step with appropriated statistical distributions- affectation. Pearson system associated with a k-means clustering algorithm and Stochastic Expectation-Maximization 'SEM' algorithm could be adapted to such problem. For each image-s class, Pearson system attributes one distribution type according to different parameters and especially the Skewness 'β1' and the kurtosis 'β2'. The different adapted algorithms, K-Means clustering algorithm, SEM algorithm and Pearson system algorithm, are then applied to satellite image segmentation problem. Efficiency of those combined algorithms was firstly validated with the Mean Quadratic Error 'MQE' evaluation, and secondly with visual inspection along several comparisons of these unsupervised images- segmentation.
Abstract: Fishing has always been an essential component of
the Polynesians- life. Fishhooks, mostly in pearl shell, found during
archaeological excavations are the artifacts related to this activity the
most numerous. Thanks to them, we try to reconstruct the ancient
techniques of resources exploitation, inside the lagoons and offshore.
They can also be used as chronological and cultural indicators. The
shapes and dimensions of these artifacts allow comparisons and
classifications used in both functional approach and chrono-cultural
perspective. Hence it is very important for the ethno-archaeologists
to dispose of reliable methods and standardized measurement of
these artifacts. Such a reliable objective and standardized method
have been previously proposed. But this method cannot be envisaged
manually because of the very important time required to measure
each fishhook manually and the quantity of fishhooks to measure
(many hundreds). We propose in this paper a detailed acquisition
protocol of fishhooks and an automation of every step of this method.
We also provide some experimental results obtained on the fishhooks
coming from three archaeological excavations sites.
Abstract: This paper used a fuzzy kohonen neural network for medical image segmentation. Image segmentation plays a important role in the many of medical imaging applications by automating or facilitating the diagnostic. The paper analyses the tumor by extraction of the features of (area, entropy, means and standard deviation).These measurements gives a description for a tumor.
Abstract: In this paper, a new automated methodology to detect the optic disc (OD) automatically in retinal images from patients with risk of being affected by Diabetic Retinopathy (DR) and Macular Edema (ME) is presented. The detection procedure comprises two independent methodologies. On one hand, a location methodology obtains a pixel that belongs to the OD using image contrast analysis and structure filtering techniques and, on the other hand, a boundary segmentation methodology estimates a circular approximation of the OD boundary by applying mathematical morphology, edge detection techniques and the Circular Hough Transform. The methodologies were tested on a set of 1200 images composed of 229 retinographies from patients affected by DR with risk of ME, 431 with DR and no risk of ME and 540 images of healthy retinas. The location methodology obtained 98.83% success rate, whereas the OD boundary segmentation methodology obtained good circular OD boundary approximation in 94.58% of cases. The average computational time measured over the total set was 1.67 seconds for OD location and 5.78 seconds for OD boundary segmentation.
Abstract: Medical image segmentation based on image smoothing followed by edge detection assumes a great degree of importance in the field of Image Processing. In this regard, this paper proposes a novel algorithm for medical image segmentation based on vigorous smoothening by identifying the type of noise and edge diction ideology which seems to be a boom in medical image diagnosis. The main objective of this algorithm is to consider a particular medical image as input and make the preprocessing to remove the noise content by employing suitable filter after identifying the type of noise and finally carrying out edge detection for image segmentation. The algorithm consists of three parts. First, identifying the type of noise present in the medical image as additive, multiplicative or impulsive by analysis of local histograms and denoising it by employing Median, Gaussian or Frost filter. Second, edge detection of the filtered medical image is carried out using Canny edge detection technique. And third part is about the segmentation of edge detected medical image by the method of Normalized Cut Eigen Vectors. The method is validated through experiments on real images. The proposed algorithm has been simulated on MATLAB platform. The results obtained by the simulation shows that the proposed algorithm is very effective which can deal with low quality or marginal vague images which has high spatial redundancy, low contrast and biggish noise, and has a potential of certain practical use of medical image diagnosis.
Abstract: This paper presents a comparative analysis of a new
unsupervised PCA-based technique for steel plates texture segmentation
towards defect detection. The proposed scheme called Variance
Based Component Analysis or VBCA employs PCA for feature
extraction, applies a feature reduction algorithm based on variance of
eigenpictures and classifies the pixels as defective and normal. While
the classic PCA uses a clusterer like Kmeans for pixel clustering,
VBCA employs thresholding and some post processing operations to
label pixels as defective and normal. The experimental results show
that proposed algorithm called VBCA is 12.46% more accurate and
78.85% faster than the classic PCA.
Abstract: Segmentation in ultrasound images is challenging due to the interference from speckle noise and fuzziness of boundaries. In this paper, a segmentation scheme using fuzzy c-means (FCM) clustering incorporating both intensity and texture information of images is proposed to extract breast lesions in ultrasound images. Firstly, the nonlinear structure tensor, which can facilitate to refine the edges detected by intensity, is used to extract speckle texture. And then, a spatial FCM clustering is applied on the image feature space for segmentation. In the experiments with simulated and clinical ultrasound images, the spatial FCM clustering with both intensity and texture information gets more accurate results than the conventional FCM or spatial FCM without texture information.
Abstract: This paper presents a new growing neural network for
cluster analysis and market segmentation, which optimizes the size
and structure of clusters by iteratively checking them for multivariate
normality. We combine the recently published SGNN approach [8]
with the basic principle underlying the Gaussian-means algorithm
[13] and the Mardia test for multivariate normality [18, 19]. The new
approach distinguishes from existing ones by its holistic design and
its great autonomy regarding the clustering process as a whole. Its
performance is demonstrated by means of synthetic 2D data and by
real lifestyle survey data usable for market segmentation.