An Automatic ROI of The Fundus Photography

Al-Balas

et al. 2022

IJECE

This paper aims to use a new technique of computed tomography (CT) scan image processing to correlate the image analysis with sinonasal symptoms. A retrospective cross-sectional study is conducted by analyzing the digital records of 50 patients who attended the ear, nose and throat (ENT) clinics at King Abdullah University Hospital, Jordan. The coronal plane CT scans are analyzed using our developed software. The purposes of this software are to calculate the surface area of the nasal passage at three different levels visible on coronal plane CT scans: i) the head of the inferior turbinate, ii) the head of the middle turbinate, and iii) the tail of the inferior turbinate. We employ image processing techniques to correlate the narrowing of nasal surface area with sinonasal symptoms. As a consequence, obstruction in the first level is correlated significantly with the symptoms of nasal obstruction while the narrowing in the second level is related to frontal headache. No other significant correlations are found with nasal symptoms at the third level. In our study, we find that image processing techniques can be very useful to predict the severity of common nasal symptoms and they can be used to suggest treatment and to follow up on the case progression.

Section: Region Of Interest (Roi) Selectionmentioning

confidence: 99%

Computed tomography scans image processing for nasal symptoms severity prediction

Al-Balas

et al. 2022

IJECE

Int. J. Adv. Sci. Eng. Inf. Technol.

“…The dataset is a color image of a diabetic retinopathy patient seeking treatment at the hospital. This dataset has been diagnosed by an ophthalmologist used in research on a different topic from this study [20]. The images were recorded using a Fundus camera brand Nonmyd7 produced by Kowa Company Ltd. Recording specifications are presented in Table 1.…”

Section: A the Input Imagementioning

confidence: 99%

Color Feature Segmentation Image for Identification of Cotton Wool Spots on Diabetic Retinopathy Fundus

Pranata

Na’am

Hidayat

2020

Fundus is an image of the inner eye surface in the form of a colored image. This image has a lot of pixel values because it consists of three basic color components. The three colors are red, green, and blue, so they need a good technique in analyzing this image. This image can be used to diagnose diabetic retinal disease caused by diabetes mellitus. This disease can interfere with human vision because objects that cover the retina of the eye is called Cotton Wool Spot (CWS). The severity of this disease can be observed from the large area of the CWS covering the retina. This study aims to calculate the exact area ratio of CWS with the retina area. The method used in this research is Image Color Feature Segmentation (ICFS). This method has four stages, namely preprocessing, segmentation, feature extraction, and feature areas. The dataset processed in this study was sourced from the Radiology Department, General Hospital of M. Djamil Padang. The dataset consists of 16 fundus images of patients who were treated at the hospital. The results of this study can identify and calculate the percentage of retinal damage is very well. Therefore, this study can be a reference in measuring the severity of diabetic retinopathy for prevention and subsequent treatment for patients and doctors.

“…A crucial tool for decision-making and treatment procedures in healthcare is now digital medical images [1]- [3]. Determining the area of interest in medical images is one of the most important basic operations in diagnostic systems [4], [5], and the reason is due to the fact that most images contain useless areas [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Region of Interest Extraction using K-Means and Edge Detection for DEXA Images

Z. Mohammed,

George

2023

AJEST

Region of interest in the world of medical images, there is a region in each image that contains unique features that distinguish each image from the other or distinguish one group of the image from another group. This paper proposes a new method for extracting the region of interest for DEXA images via the K-means and edge detection. firstly, the noise is reduced by the mean filter then segment the image into two clusters by kmeans, followed by edge detection to identify object boundaries and erosion operation to clarify boundaries and get the correct coordinates of ROI. The results show that the accuracy of the proposed system is 99%. where 174 images cropped correctly out of 176. the dataset used in this work is 'Osteoporosis DEXA Scans Images of Spine from Pakistan'.