Color Image Segmentation using ERKFCM

Mythili, C.; Kavitha, V.

doi:10.5120/5809-8074

Cited by 13 publications

(4 citation statements)

References 15 publications

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“…Another technique Salt and pepper is used for noise removal. Salt and pepper noise distorts images during quick transients, such as faulty switching places [19]. In Gaussian noise the original value of each pixel in the image will change by a small amount.…”

Section: Review Of Existing Literaturementioning

confidence: 99%

Optimizing MRI Preprocessing Techniques for Enhanced Alzheimer’s Disease Detection

Al Khayari,

Abdallah Nasser Al Shamsi

2024

IJEMD-CSAI

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Alzheimer’s disease (AD) presents an acute global health challenge, with a sharp rise in cases prompting urgent action from the healthcare community. Recognizing the potential of Artificial Intelligence (AI) in early detection and treatment planning, researchers are focusing on optimizing MRI scans, a key diagnostic tool. However, MRI images often suffer from distortions like motion artifact and intensity fluctuations, compromising AI predictions. This study investigates MRI artifacts and proposes preprocessing solutions such as reorientation, registration, skull stripping, and slicing to enhance image quality. Integration into a user-friendly GUI aims to streamline healthcare operations, empowering medical professionals with accurate AD predictions. This research advances early detection efforts, improving patient outcomes and fostering innovation in medical imaging.

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Section: Review Of Existing Literaturementioning

confidence: 99%

Optimizing MRI Preprocessing Techniques for Enhanced Alzheimer’s Disease Detection

Al Khayari,

Abdallah Nasser Al Shamsi

2024

IJEMD-CSAI

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“…In [7], the authors proposed an approach where they combine segmentation of various color spaces such as RGB, HSV, YIQ and XYZ to give more accurate segmentation result compared to segmentation of single color space. K-Means and Effective robust kernelized fuzzy c-means (ERKFCM) are used to segment the images.…”

Section: Review Of Literaturesmentioning

confidence: 99%

An Efficient Approach toward Color Image Segmentation with Combined Effort of Soft Clustering and Region based Techniques using L Channel of LAB Color Space

Bora¹,

Gupta²,

Khan³

2015

IJCA

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Finding an efficient approach for color image segmentation is always sought by the researchers in the color image processing research. We have different clustering based and region based methods for the same. But still there arises the requirement of an optimal method. In this paper, a new approach for color image segmentation is proposed. Here the segmentation is carried out on the L channel of LAB color space. The input color image is first converted from RGB to LAB. Then L channel is extracted from the LAB converted image and sent as input to FCM algorithm. After this initial segmentation, the segmented image is filtered by sobel filter. The filtered image is then segmented by Meyer's Watershed algorithm to produce the final segmented image of the original image. The results of the proposed approach are found efficient when the same are analyzed in terms of MSE and PSNR. Also the segmented images are found free from over segmentation.

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“…Several implementations of traffic sign detection and recognition systems used different algorithms in pre-processing, detection and recognition phases. Pre-processing stage is necessary to suppress noise and improve image performance [8]. A study [6] showed the detection improvements in traffic sign recognition provided by the use of pre-processing and filtering methods by means of contrast stretching, color normalization and image enhancement before getting the regions of interest.…”

Section: Introductionmentioning

confidence: 99%

“…A study [11] solved this problem by instead of working on absolute RGB values, relative RGB values are used for they are almost unchanged with various illumination circumstances. Another study [8] combined four color spaces namely RGB, HSV, YIQ, and XYZ based on histogram to provide more accurate segmentation. Color is segmented using K-means clustering and effective robust kernel based Fuzzy C-means clustering resulting to a more accurate segmentation but has increased computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Traffic Sign Detection and Recognition System for Assistive Driving

Santos¹,

Abu²,

Oppus³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Road traffic accidents are primarily caused by drivers error. Safer roads infrastructure and facilities like traffic signs and signals are built to aid drivers on the road. But several factors affect the awareness of drivers to traffic signs including visual complexity, environmental condition, and poor drivers education. This led to the development of different ADAs like TSDR that enhances vehicle system. More complex algorithms are implemented for improvement but this affects the performance of a real-time system. This study implements a real-time traffic sign detection and recognition system with voice alert using Python. It aims to establish the proper trade-off between accuracy and speed in the design of the system. Four pre-processing and object detection methods in different color spaces are evaluated for efficient, accurate, and fast segmentation of the region of interest. In the recognition phase, ten classification algorithms are implemented and evaluated to determine which will provide the best performance in both accuracy and processing speed for traffic sign recognition. This study has determined that Shadow and Highlight Invariant Method for the pre-processing and color segmentation stage provided the best trade-off between detection success rate (77.05%) and processing speed (31.2ms). Convolutional Neural Network for the recognition stage not only provided the best trade-off between classification accuracy (92.97%) and processing speed (7.81ms) but also has the best performance even with lesser number of training data. Embedded system implementation utilized Nvidia Jetson Nano with interface Waveshare IMX219-77 camera, Nvidia 7" LCD and generic speaker and programmed in Python with OpenCV, sci-kit learn and Pytorch libraries. It is capable of running at an adaptive frame rate from 8-12 frames per second with no detection and down to approximately 1 frame per second when there is traffic sign detected.

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Color Image Segmentation using ERKFCM

Cited by 13 publications

References 15 publications

Optimizing MRI Preprocessing Techniques for Enhanced Alzheimer’s Disease Detection

Optimizing MRI Preprocessing Techniques for Enhanced Alzheimer’s Disease Detection

An Efficient Approach toward Color Image Segmentation with Combined Effort of Soft Clustering and Region based Techniques using L Channel of LAB Color Space

Real-Time Traffic Sign Detection and Recognition System for Assistive Driving

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