This paper proposes a new approach for the segmentation of the side face images to obtain the ear region. The proposed approach is divided into two basic steps: The first step classifies the image pixels into skin and non-skin pixels using likelihood
skin detector.
This likelihood image is processed by using morphological operations to detect the ear region. In the second step, image containing ear region is isolated from side face image by using one of two methods; the first is based on experiment, while the second is based measurements. The study includes a comparison of the results between the proposed study and previous ones to identify the differences. The proposed approach is applied on a database containing 146 images of 20 persons. These images were taken under different illumination, pose, day, and location variations. The partial occlusion by hair or earing was also taken in account. The results showed that the system achieved a correct segmentation
with rate 95.8%.
facial characteristic points-FCP
neuro_fuzzy controller
Morphological Operations
Pattern recognition
Ear image
Ear shape
Ear detection
Ear segmentation
Ear recognition
Skin detection
Likelihoo
تعرف النماذج
صورة الأذن
كشف الأذن
اقتطاع منطقة الأذن
تعرف الأشخاص باستخدام الأذن
كشف الجلد
العمليات المورفولوجية
الأرجحية
المزيد..
This paper proposes a new approach for the segmentation of the retina images to obtain the optic nerve and blood vessels regions. We used retinal images from DRIVE and STARE databases which include different situations like illumination variations, d
ifferent optic nerve positions (left, right and center). Illumination problem has been solved by preprocessing stage including image histogram-based illumination correction. Next, some morphological operations were used to filter the preprocessed image to obtain the ROI region, then, the center and radius of optic nerve were determined, and the optic nerve region was extracted from the original image. In blood vessels segmentation, we applied the illumination correction and median filtering.Then the closing, subtraction and morphological operations were done to get the blood vessels image which was thresholded and thinned to get the final blood vessels image.
This research aims to developing new method for breast tumors extraction and
features detection in breast magnetic resonance images by depending on clusteringand
image processing algorithms. At the beginning, one of clustering algorithms was used f
or
image segmentation and grouping pixels by their gray scale values. Then morphological
operations were implemented in order to remove noise and undesired regions, after that
suspected areas were extracted. Finally some shape features for extracted area were
detected, this features could be very useful for tumors diagnosis. A database consisted of
96breast magnetic resonance images were used and proposed approach was appliedby
MATLAB program, and we obtainedbreast tumors extraction and its features and
compared them with the doctor's opinion .
Considered the diagnosis of diseases using image processing is one of the most
important areas of image processing techniques used in the medical field, Where is the
digital data in the field of ophthalmology focus of researchers for automatic dete
ction of
some important diseases such as diabetic retinopathy (DR).
And is defined as damage to the retina of the eye comes as serious complications and
on the human body complications resulting from diabetes in the long term and is
considered one of the most important causes of blindness in the world and cause serious
damage to the retina.
The research aims to Assess the performance of some of the methods used in the
diagnosis of diabetic retinopathy by revealing one of the most important accompanying
pests him in the retina of the eye and is the exudates and through diagnosed in images
digital fundus through image processing techniques where this detection process
contributes in helping to early detection.
In this paper, we present a new algorithm to automate the detection
and extraction of buildings from satellite images, this algorithm is
distinguished since it overcomes some obstacles that limit detecting
within other methods, such as the differe
nce in shape, color, and
height of buildings, and it doesn't need multi-spectral images or other
complex and high cost images.
This Paper offers an effective method to measure the length of the
femur in Fetal Ultrasound Images, it applies a series of steps
starting with the reducing amount of noise in these images, and
then converted them to a binary form and uses morphol
ogical
operations to segment the femur and isolate it from the rest of the
image objects, then it applies an Edge Detector in order to find the
edges of the bone, then uses the Hough Transform to detect straight
lines in the image. we apply overlapping for resulted lines on the
original image, finally we choose the most significant and longest
straight line which is corresponding to the length of the femur. The
proposed method facilitates the measurement of the femur without
the help of a physician through a series of steps.