Study of the influence of removing speckle noise on the segmentation of uterus ultrasonic images based on Active shape contour algorithm

The study suggests a new approach to segment the ultrasound uterus images to obtain the fetus region. The approach consists of three stages. The first includes the preprocessing in which the speckle noise is removed from the ultrasound images depending on sequential filtering of Gabor filter and median filter. Second, an improved active shape contour independent of edges is applied to segment the uterus images. The last stage is the post processing which depends on the morphological operation to eliminate the undesired region and obtain the region of interest (fetus). The designed system has been tested by means of medical database of ultrasound uterus images downloaded from the ULTRASCAN CENTRE site in Kaloor (India). The experimental tests show that the proposed sequential filtering technique improves the active shape contour algorithm performance significantly, so the system segment the uterus images correctly even in the presence of speckle noise.

Ear segmentation using likelihood skin detector and morphological operations

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%.

Retina Segmentation to Obtain the Optic Nerve Area and Blood Vessels

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, different 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.

Breast tumors extraction and features detection in breast magnetic resonance images using clustering and image processing algorithms

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 for 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 .

Assess the performance of the diagnosis ways of diabetic retinopathy

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 detection 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.

Buildings detection in Satellite images

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 difference in shape, color, and height of buildings, and it doesn't need multi-spectral images or other complex and high cost images.

Auto-Detection of Femur Length Using Ultrasound Image Processing Techniques

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 morphological 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.