Integration of Marker Controlled Watershed and Region Merging Method for Image Segmentation

Automatic image segmentation is a veryimportant task for image analysis, object detectionand recognition tasks. In this research, automaticimage segmentation system is proposed whichincludes three main approaches: preprocessing,segmentation and post processing approach. Thepreprocessing step estimates a better approximationof gradient magnitudes by the modified 7x7Laplacian of Gaussian (LoG) edge filter. Insegmentation step, marker controlled watershedmethod (MCWS) is applied to solve oversegmentation problem. Finally, the segmentedregions are merged by using histogram similarity toobtain the accurate segmented regions in an image.This system is tested on two different kinds ofdatasets: medical image dataset and color naturalimage dataset. In this research, this system has alsoachieved accuracy 93.01% for brain image, 76.72%for color natural image. The running time of theproposed system takes five times than MCWS methodfor medical images due to region merging process formany complex regions

Automatic Natural Image Segmentation by Using MarkerControlled Watershed Method and Region Merging Method

Fully automatic image segmentation is adifficult task for natural images because of manyvariations ascontrast and complex background.Conventional segmentation methods require aconsiderable amount of interactive guidance by theuser to attain satisfactory results. Moreover, the mostsubsequent tasks as object detection and imageanalyzing application highlydepend on the accurateand useful segmented result. Therefore, in this paper,an automatic image segmentation method for naturalimages is proposed. The proposed system includesthree approaches: gradient computation with themodified LoG edge filter, marker-controlled watershedsegmentation(MCWS) with automatically markerselection and region mergingapproach that is based onedge strength and homogeneous intensity. The systemcan not only efficiently reduce the significant oversegmentation problem of watershed algorithm and butalso produce the correct and meaningful segmentedimagesIt purposes better performance of segmentedimages for image annotation, objects detection, imageanalyzing task and computer vision

Urodynamic Assessment in Postmenopausal Women

Lower urinary tract (LUT) symptoms are not uncommon in postmenopausal&nbsp;Myanmar women. The aim of the study was to study the urodynamic&nbsp;assessment in postmenopausal women. A cross-sectional, analytical study&nbsp;was conducted to eligible 114 postmenopausal women (81 symptomatic&nbsp;women and 33 asymptomatic women) attending at Gynaecological Out-patient Department of Central Women&rsquo;s Hospital (Yangon) from&nbsp;October 2012 to June 2014. Examination included body mass index (BMI),&nbsp;vaginal examination, cough test, and 1 hour pad test. Urine RE was done to&nbsp;all patients to exclude urinary tract infection. Urodynamic investigation was&nbsp;done in those patients who had no urinary tract infection and it was carried&nbsp;out at Urosurgical Ward of No. 2 Military Hospital, Yangon. The most&nbsp;frequent symptom among the study population was urinary incontinence&nbsp;(50%) followed by urgency (45.6%), night time frequency (38.6%).&nbsp;Symptom-based clinical diagnosis of study population were: stress urinary&nbsp;incontinence (SUI) in 9.6%, urgency urinary incontinence (UUI) in 4.9%,&nbsp;mixed urinary incontinence (MUI) in 25.4%, overactive bladder (OAB) in&nbsp;16.7%, and voiding problem in 28.9% of study population.</p