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Automatical IVUS Image Segmentation and Detection Using BLOB Analysis
(2022-01-01)
Onhirun, Nippitch
;
Charoenporn, Pattama
In this research, experiments were conducted on the problem of image analysis in atherosclerotic disease. The study was conducted from intravascular ultrasound images that have been taken inside the blood vessels from patients with arterial wall hardening or stenosis. These images are used by experts or specialists to analyze the problem of the disease. There may be some interference from shadows or elements that occurred while taking the image that may cause the incomplete image. We propose an applied method by using computer techniques that help to analyze the components within the image to find the area of interest by enhancement image. Then, the pixel levels are analyzed and classified to divide the background from the image. Next, the morphological operation is used to readjust image properties, and blob analysis is used to identify the region of interesting pixel values. In the final process, Connected Component Analysis (CCA) was used to detect media-adventitia and lumen boundary area The efficacy of segment results was measured by comparing with the expert to measure performance as (media-adventitia, lumen) the Jaccard Index = (0.9570, 0.96695), Hausdorff Distance = (0.5259, 0.6304), Percentage Area Distance = (0.0382, 0.03395) IVUS dataset was used from Simone Balocco, Dept. Matemàtica Aplicada i Anàlisi, Universitat de Barcelona, Barcelona, Spain.
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Automated Screening of Cervical Cancer Cell Images
(2019-01-10)
Sangworasil, Manas
;
Sukkasem, Chayanisa
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Sasivimolkul, Suvicha
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Suvarnaphaet, Phitsini
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Pechprasarn, Suejit
Cervical cancer is the first-most common type of female cancer and the second leading cause of death in Thailand. The number of cervical cancer is increasing in every year, even though it is preventable by the screening in early detection. The most popular method for the screening is so-called Pap smear test via examining morphology change in cervix cells. The aim of this research is to implement an image processing algorithm for classifying Pap smear cell images by calculating nucleus-to-cytoplasm area ratio. The algorithm used to classify the nucleus was mathematically calculated through k-mean clustering. The cytoplasm area was calculated from its edge profile relating to geometrical rotation method. Finally, the abnormal cells can be segmented using the area of nucleus-to-cytoplasm ratio with the accuracy of detection at 79%.
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Morphology-based algorithm and application for mammographic masses segmentation
(2013-01-01)
Suapang, Piyamas
;
Naruephai, Chadaporn
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Chivapreecha, Sorawat
Medical images segmentation is an important work for object recognition of the human organs and it is an important pre-processing step in medical image segmentation and 3D reconstruction. Conventionally, segmentation is detected according to some early brought forward algorithms such as gradient-based algorithm and template-based algorithm, but they are not so good for noise medical image segmentation. In this paper, basic morphological theory and operations are introduced at first, and then a novel morphological segmentation algorithm is proposed to detect the segment of mammographic masses with salt-And-pepper noise. The experimental results show that the proposed algorithm is more efficient for medical image denoising and segmentation than the usually used template-based segmentation algorithms and general morphological segmentation algorithms. Furthermore, the application is helpful for physician and doctors in diagnosis of the breast cancer in further steps.
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Mammographic masses segmentation based on morphology
(2012-12-01)
Suapang, Piyamas
;
Naruephai, Chadaporn
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Thongyoun, Methinee
;
Chivaprecha, Sorawat
Medical images segmentation is an important work for object recognition of the human organs and it is an important pre-processing step in medical image segmentation and 3D reconstruction. Conventionally, segmentation is detected according to some early brought forward algorithms such as gradient-based algorithm and template-based algorithm, but they are not so good for noise medical image segmentation. In this paper, basic morphological theory and operations are introduced at first, and then a novel morphological segmentation algorithm is proposed to detect the segment of mammographic masses with salt-and-pepper noise. The experimental results show that the proposed algorithm is more efficient for medical image denoising and segmentation than the usually used template-based segmentation algorithms and general morphological segmentation algorithms. ©2012 IEEE.
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Segmentation of magnetic resonance images using discrete curve evolution and fuzzy clustering
(2007-12-01)
Supot, Sookpotharom
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Thanapong, Chaichana
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Chuchart, Pintavirooj
;
Manas, Sangworasil
The region clustering of a Magnetic Resonance Imaging (MRI) image is more complicate than a Computed Topography (CT) image because a MRI image composes of three components such as T1-weighted, T2-weighted, and Proton Density (PD) in each layer. However, the MRI images provide more detail than the CT images. Therefore, we propose a technique of the region clustering of MRI image by using Fuzzy c-means (FCM). The fuzzy c-means algorithm is an iterative operation, that is very time-consuming and makes the algorithm impractical for using in image segmentation. To cope with this problem, the discrete curve evolution (DCE) technique is applied to find the actual cluster center to refine the initial value of the fuzzy c-means algorithm, which reduces the convergence time. In experimental results, the proposed technique provides the same segmentation accuracy as the fuzzy c-means technique. Moreover, this technique takes lower computational time comparing to the previous method. © 2007 IEEE.
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SAR segmentation using anisotropic diffusion
(2005-12-01)
Intajag, Sathit
;
Tipsuwanpon, Vittaya
Speckle effects are commonly observed in a synthetic aperture radar (SAR) image, which applies in many land applications, such as agriculture or forestry. However, although the human eye is able to derive the meaningful information from the SAR image, automatic processing is very difficult to distinguish the objects in the images because of the noise effect present in the image. This paper presents a segmentation method of the SAR images, which uses an anisotropic diffusion algorithm with an inherence region-growing algorithm. Anisotropic diffusion is modified in logarithmic domain to remove the speckle noise and to identify the homogeneous regions in ERS and JERS satellite data. The modified diffusion method is formulated with a tensor technique, varying with both the location of a pixel in the image and the orientation of the local image geometry vicinity the pixels. In our algorithm, SAR images are transformed into logarithmic domain and using the modified anisotropic diffusion to grow the homogeneous regions in the noise environment; consequently, the segmented image in the logarithm domain is converted to the intensity domain by an exponential function. On the other hand, our diffusion algorithm can stop itself by a standard deviation to mean criteria. Simulation results on the satellite images are provided to indicate the effectiveness of the proposed method when used to remove the effects of speckle noise almost as well as that of the additive noise. The evaluation of the proposed method employs the theoretical gain in equivalent numbers of looks (ENL). © 2005 SICE.
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Enhancement of color image obtained from PCA-FCM technique using local area histogram equalization
(2002-12-01)
Chitwong, S.
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Boonmee, T.
;
Cheevasuvit, F.
This paper presents local area enhancement of the segmented color image obtained from the multi-spectral image clustering by using FCM (fuzzy c-means). In case, the multi-spectral images, which have the number of bands more than that of 3, must decrease the data volume to remain the number of bands of 3 in order to correspond with the meaning of red, green, and blue images. PCA (Principal Components Analysis) is then used to transform original multi-spectral images into PCA images. The first three components having information more than that of original images of 95% is assigned as red, green, and blue images, namely RGB color image. FCM clustering apply to RGB color image, separately. This method is called the PCA-FCM technique being the multi-spectral image clustering. By applying such technique, the result images consisted of red, green, and blue images separately are the segmented images. By histogram equalization algorithm, the result of local area enhancement based on a number of clusters as the segmented image can solve effect of intensity saturation from global area enhancement and the perceptibility of color image is clearly improved.

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