Histogram Equalization for Improving Quality of  Low-resolution Ultrasonography Images

Supriyanti, Retno; Priyono, Subkhi Adhi; Murdyantoro, Eko; Widodo, Haris Budi

doi:10.12928/telkomnika.v15i3.5537

Cited by 4 publications

(3 citation statements)

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“…CII equal to one indicates no enhancement. The fundus images used in this paper is collected from High-Resolution Fundus (HRF) Image Database, maintained byPattern Recognition Lab, Department of Informatik, Friedrich-Alexander-Universität, Germany [21][22][23][24][25]. The images belong to glaucoma patients acquired with a Canon CR-1 fundus camera with a field of view of 45°.…”

Section: 𝑇(𝑘) = 𝐶(𝑘)(𝐿 − 1) 𝐺𝑖𝑣𝑒𝑛 𝐶(𝑘) = ∑ 𝑊(𝑘) 𝑘 𝑖=0mentioning

confidence: 99%

A novel equalization scheme for the selective enhancement of optical disc and cup regions and background suppression in fundus imagery

Shamsudeen¹,

Raju

2019

TELKOMNIKA

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The ratio of the diameters of Optic Cup (OC) and Optic Disc (OD), termed as 'Cup to Disc Ratio' (CDR), derived from the fundus imagery is a popular biomarker used for the diagnosis of glaucoma. Demarcation of OC and OD either manually or through automated image processing algorithms is error prone because of poor grey level contrast and their vague boundaries. A dedicated equalization which simultaneously compresses the dynamic range of the background and stretches the range of ODis proposed in this paper. Unlike the conventional GHE, in the proposed equalization, the original histogram is inverted and weighted nonlinearly before computing the Cumulative Probability Density (CPD). The equalization scheme is compared with Adaptive Histogram Equalization (AHE), Global Histogram Equalization (GHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) in terms of the difference between the mean grey levels of OD and the background, using a quantitative metric known asContrast Improvement Index (CII). The CII exhibited by CLAHE, GHE and the proposed scheme are 1.1977 ± 0.0326, 1.0862 ± 0.0304 and 1.3312 ± 0.0486, respectively.The proposed method is observed to be superior to CLAHE, GHE and AHE and it can be employed in Computerized Clinical Decision Support Systems (CCDSS) to improve the accuracy of localizing the OD and the computation of CDR.

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Section: 𝑇(𝑘) = 𝐶(𝑘)(𝐿 − 1) 𝐺𝑖𝑣𝑒𝑛 𝐶(𝑘) = ∑ 𝑊(𝑘) 𝑘 𝑖=0mentioning

confidence: 99%

A novel equalization scheme for the selective enhancement of optical disc and cup regions and background suppression in fundus imagery

Shamsudeen¹,

Raju

2019

TELKOMNIKA

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“…Memon [25] applied the Median filter to remove noise in the microscopic image. Supriyanti [26] implemented the histogram equalization method to normalize input images. However, in this paper, we emphasize the discussion on coronal slice analysis only in exploring the morphological information in it.…”

Section: Introductionmentioning

confidence: 99%

Coronal slice segmentation using a watershed method for early identification of people with Alzheimer's

Supriyanti¹,

Marchel²,

Ramadhani³

et al. 2021

TELKOMNIKA

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One physical sign of a person who has Alzheimer's is the diminution of the area of the hippocampus and ventricles. A good quality magnetic resonance imaging (MRI) will provide a high-quality image so that the doctor will quickly analyze the abnormalities of the hippocampus and ventricle area. However, for low-quality MRI, this is difficult to do. This condition will be a significant problem for some regions in developing countries including Indonesia, where many hospitals have only low-quality MRI, and many hospitals do not have them at all. The primary purpose of this research is to develop simple tools to analyze morphological characteristics in Alzheimer's patients. In this paper, we focus only on coronal slice analysis. We will use watershed method segmentation, because of this method able to segment the boundaries automatically, so that parts of the hippocampus and ventricles can be identified in an MRI image. Analysis of morphological characteristics is also classified by age and gender. Then by referring to the value of the clinical dementia rating (CDR), the process of identifying between images with Alzheimer's disease (AD) and healthy models is done based on the morphological analysis that has been done. The results show this method has a better performance compared to the previously work.

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“…One solution to solve this problem is the use of digital image processing techniques for morphology identification of leukocyte cells. In our previous research [3]- [9] we developed simple and easy-to-use system for handling problems about health services in rural areas by implementing image processing techniques in the case of cataract diseases, pregnancy diagnosis, and dental segmentation.…”

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confidence: 99%

Characteristics Identification of Myeloblast Cell Using K-Means Clustering for Uncontrolled Images

Supriyanti¹,

Rifa’i²,

Ramadhani³

et al. 2019

IJMLC

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Blood cells are one of the most important parts in humans. One type of blood cells that play an important role in a leukemia diagnosis is leukocyte cells. There are some types of leukocyte i.e. myeloblast, lymphoblast, monoblasts and erythroblasts. One method of measuring leukocyte cell abnormalities is by examination of the morphology of leukocyte cells covering the area, circumference and diameter of leukocyte cells. In this research will be identified morphology of myeloblast cells by using K-means clustering method. The observed control variable is a characteristic of myeloblast cell that includes diameter, contour, and uniformity of object, amount, and cell density. While the observed data is uncontrolled image (noise) with RGB color format. The experiment showed promising results for further development.

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Histogram Equalization for Improving Quality of Low-resolution Ultrasonography Images

Cited by 4 publications

References 0 publications

A novel equalization scheme for the selective enhancement of optical disc and cup regions and background suppression in fundus imagery

A novel equalization scheme for the selective enhancement of optical disc and cup regions and background suppression in fundus imagery

Coronal slice segmentation using a watershed method for early identification of people with Alzheimer's

Characteristics Identification of Myeloblast Cell Using K-Means Clustering for Uncontrolled Images

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