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In histopathology images, there often exists several Nuclei overlapped with each other which causes difficulty to automatic nuclei segmentation. As we all know, watershed algorithm has been widely employed in image segmentation. But the limitation of watershed segmentation is sensitive to noise and can lead to serious over-segmentation. In this paper, we present an improved watershed transformation...
Medulloblastoma (MB) is the most common brain tumor in children. Recent studies have demonstrated a relationship between specific signaling pathway abnormalities, a tendency to more favorable outcomes, and a histopathological feature: nodular growth patterns. In this work we present a new segmentation scheme which requires minimal user interaction to segment nodules on MB histopathological sections...
With the sophistication in automated computing systems Bio-Medical Image analysis is made simple. Today there is an increase in interest for setting up medical system that can screen a large number of people for sight threatening diseases, such Retinoblastoma (Rb) and Diabetic Retinopathy(DR). Spatial Domain Edge Detection approach needs Gray scale images for feature extraction and highly prone to...
Melanoma can be cured if it is detected early, so early diagnosis is very important in dermatological practice today. Early and non-invasive diagnosis of melanomas can be done by accurate image segmentation of skin lesions. The medical images, while acquisition are generally bound to contain noise. This paper proposes a robust and efficient image segmentation algorithm using LOG edge detector to extract...
In this paper we present an unsupervised automatic method for segmentation of nuclei in H&E stained breast cancer biopsy images. Colour deconvolution and morphological operations are used to preprocess the images in order to remove irrelevant structures. Candidate nuclei locations, obtained with the fast radial symmetry transform, act as markers for a marker-controlled watershed segmentation....
This paper presents an algorithm to classify pixels in uterine cervix images into two classes, namely normal and abnormal tissues, and simultaneously select relevant features, using group sparsity. Because of the large variations in image appearance due to changes of illumination, specular reflections and other visual noise, the two classes have a strong overlap in feature space, whether features...
We propose an automatic color segmentation system that (1) incorporates domain knowledge to guide histological image segmentation and (2) normalizes images to reduce sensitivity to batch effects. Color segmentation is an important, yet difficult, component of image-based diagnostic systems. User-interactive guidance by domain experts-i.e., pathologists-often leads to the best color segmentation or...
The proper segmentation of the vascular system of the retina currently attracts wide interest. As a precious outcome, a successful segmentation may lead to the improvement of automatic screening systems. Namely, the detection of the vessels helps the localization of other anatomical parts and lesions besides the vascular disorders. In this paper, we recommend a novel approach for the segmentation...
This paper proposes a messy model based watermarking scheme for the authentication of medical images. The digital fundus images are one particular class of medical images which has been chosen for simulation and analysis of the proposed scheme in this paper. These images are given in TIF format in RGB color space. The proposed scheme dynamically generates the watermark using messy models. And, it...
The early detection of the lung cancer is a challenging problem, due to the structure of the cancer cells. This paper presents two segmentation methods, Hopfield Neural Network (HNN) and a Fuzzy C-Mean (FCM) clustering algorithm, for segmenting sputum color images to detect the lung cancer in its early stages. The manual analysis of the sputum samples is time consuming, inaccurate and requires intensive...
Color is that attribute of light-energy which is related to the wavelength. It is well known that color carries a very important part of information regarding objects of interest in an image. This talk introduces the audience to the representation of color (color-models) and color-based segmentation of images, with several illustrative examples from the speaker's research-experience in this area over...
Tuberculosis (TB) is a communicable disease for which early diagnosis is critical for disease control. Manual screening for TB identification involves a labor-intensive task with poor sensitivity and specificity. To improve the diagnostic process we develop an automated system for TB identification, which consists of an automatic microscope, an image-based autofocus algorithm and an image-based TB...
This paper presents an optimized normalized cut method for segmentation of RBCs infected with malarial parasites using peripheral blood smears. The algorithm is applied over various color spaces to find its optimal performance for microscopic blood smear images. We tested the efficacy of results in RGB, YCbCr, HSV and NTSC using the Rand's Index. The work is useful in telepathology applications and...
Abnormalities in the retinal vessel tree are associated with different pathologies. Usually, they affect arteries and veins differently. In this regard, the arteriovenous ratio(AVR) is a measure of retinal vessel caliber, widely used in medicine to study the influence of these irregularities in disease evolution. Hence, the development of an automatic tool for AVR computation as well as any other...
Clinical research suggests that changes in the retinal blood vessels (e.g., vessel caliber) are important indicators for earlier diagnosis of diabetes and cardiovascular diseases. Reliable vessel detection or segmentation is a prerequisite for quantifiable retinal blood vessel analysis for predicting these diseases. However, the segmentation of blood vessels is complicated by its huge variations such...
In this work, we introduce a computational approach that automatically detects stained follicles in IHC-stained follicular lymphoma slides using various biomarkers. This novel approach is to process whole-slide and Giga-byte scaled pathology images at multi-resolution levels. The average segmentation accuracy achieves at 83.09±6.25%. Such a computerized analysis of images is expected to provide a...
Cervix cancer is the most common gynecological malignancy and second most common cancer among female in Malaysia after breast cancer. The objective of this study is to extract the size of nucleus and cytoplasm, as well as gray level values of cervical cells from ThinPrep images so that accurate value of those parameters can easily be obtained. An alternative approach of extracting features for Pap...
Contrast enhancement and image segmentation play an important process in most medical image analysis tasks. One of the main tasks is the analyzing of white blood cells (WBC) where the WBC composition reveals important diagnostic information of a patient. This paper presents a two phase methodology in order to obtain a fully segmented abnormal white blood cell (blast) and nucleus in acute leukaemia...
Blood cell segmentation is a crucial part of many medical and laboratory procedures such as cell counting and blood cell disorder diagnosis. Among different types of blood cells, white blood cells are the most important clinically, as they suffer greatest from blood disorders. In this paper we propose a method for automatic segmentation of white blood cells nucleus. A distinctive function is used...
High-speed, bright-field (BF) microscopy of the beating embryonic zebrafish heart reveals both static background structures as well as the rapid motion of cardiac tissues and red blood cells (RBCs). However, all structures contribute to BF image contrast in a similar way, making labeling and subsequent analysis of these images difficult. Here, we report on our progress to separate cardiac BF images...
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