Abstract: In some applications, such as image recognition or
compression, segmentation refers to the process of partitioning a
digital image into multiple segments. Image segmentation is typically
used to locate objects and boundaries (lines, curves, etc.) in images.
Image segmentation is to classify or cluster an image into several
parts (regions) according to the feature of image, for example, the
pixel value or the frequency response. 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. The result of image segmentation is a set of segments
that collectively cover the entire image, or a set of contours extracted
from the image. Several image segmentation algorithms were
proposed to segment an image before recognition or compression. Up
to now, many image segmentation algorithms exist and be
extensively applied in science and daily life. According to their
segmentation method, we can approximately categorize them into
region-based segmentation, data clustering, and edge-base
segmentation. In this paper, we give a study of several popular image
segmentation algorithms that are available.
Abstract: In this paper, we present a new segmentation approach
for focal liver lesions in contrast enhanced ultrasound imaging. This
approach, based on a two-cluster Fuzzy C-Means methodology,
considers type-II fuzzy sets to handle uncertainty due to the image
modality (presence of speckle noise, low contrast, etc.), and to
calculate the optimum inter-cluster threshold. Fine boundaries are
detected by a local recursive merging of ambiguous pixels. The
method has been tested on a representative database. Compared to
both Otsu and type-I Fuzzy C-Means techniques, the proposed
method significantly reduces the segmentation errors.
Abstract: This paper presents a new technique for detection of
human faces within color images. The approach relies on image
segmentation based on skin color, features extracted from the two-dimensional
discrete cosine transform (DCT), and self-organizing
maps (SOM). After candidate skin regions are extracted, feature
vectors are constructed using DCT coefficients computed from those
regions. A supervised SOM training session is used to cluster feature
vectors into groups, and to assign “face" or “non-face" labels to those
clusters. Evaluation was performed using a new image database of
286 images, containing 1027 faces. After training, our detection
technique achieved a detection rate of 77.94% during subsequent
tests, with a false positive rate of 5.14%. To our knowledge, the
proposed technique is the first to combine DCT-based feature
extraction with a SOM for detecting human faces within color
images. It is also one of a few attempts to combine a feature-invariant
approach, such as color-based skin segmentation, together with
appearance-based face detection. The main advantage of the new
technique is its low computational requirements, in terms of both
processing speed and memory utilization.
Abstract: The purpose of this study is to introduce a new
interface program to calculate a dose distribution with Monte Carlo method in complex heterogeneous systems such as organs or tissues
in proton therapy. This interface program was developed under
MATLAB software and includes a friendly graphical user interface
with several tools such as image properties adjustment or results display. Quadtree decomposition technique was used as an image
segmentation algorithm to create optimum geometries from Computed Tomography (CT) images for dose calculations of proton
beam. The result of the mentioned technique is a number of nonoverlapped
squares with different sizes in every image. By this way
the resolution of image segmentation is high enough in and near
heterogeneous areas to preserve the precision of dose calculations
and is low enough in homogeneous areas to reduce the number of
cells directly. Furthermore a cell reduction algorithm can be used to combine neighboring cells with the same material. The validation of this method has been done in two ways; first, in comparison with experimental data obtained with 80 MeV proton beam in Cyclotron
and Radioisotope Center (CYRIC) in Tohoku University and second, in comparison with data based on polybinary tissue calibration method, performed in CYRIC. These results are presented in this paper. This program can read the output file of Monte Carlo code while region of interest is selected manually, and give a plot of dose distribution of proton beam superimposed onto the CT images.
Abstract: This paper presents a new approach for image
segmentation by applying Pillar-Kmeans algorithm. This
segmentation process includes a new mechanism for clustering the
elements of high-resolution images in order to improve precision and
reduce computation time. The system applies K-means clustering to
the image segmentation after optimized by Pillar Algorithm. The
Pillar algorithm considers the pillars- placement which should be
located as far as possible from each other to withstand against the
pressure distribution of a roof, as identical to the number of centroids
amongst the data distribution. This algorithm is able to optimize the
K-means clustering for image segmentation in aspects of precision
and computation time. It designates the initial centroids- positions
by calculating the accumulated distance metric between each data
point and all previous centroids, and then selects data points which
have the maximum distance as new initial centroids. This algorithm
distributes all initial centroids according to the maximum
accumulated distance metric. This paper evaluates the proposed
approach for image segmentation by comparing with K-means and
Gaussian Mixture Model algorithm and involving RGB, HSV, HSL
and CIELAB color spaces. The experimental results clarify the
effectiveness of our approach to improve the segmentation quality in
aspects of precision and computational time.