A Novel Method to Detect the Tumor Using Low-Contrast Image Segmentation Technique

2021 ◽  
pp. 161-170
Author(s):  
Priyanka Kaushik ◽  
Rajeev Ratan
Keyword(s):  
Image Segmentation ◽  
Low Contrast ◽  
Segmentation Technique ◽  
Novel Method ◽  
Contrast Image

scholarly journals Automated grading of mangoes based on surface defect detection using a combined approach of image segmentation

2020 ◽  
pp. 17-23
Author(s):  
Neeraj Kumari ◽  
Ashutosh Kumar Bhatt ◽  
Rakesh Kumar Dwivedi ◽  
Rajendra Belwal
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Image Segmentation ◽  
Surface Defect ◽  
Input Image ◽  
Segmentation Technique ◽  
Segmentation Accuracy ◽  
Surface Defect Detection ◽  
Automated Grading ◽  
Processing Accuracy

Image segmentation is an essential and critical step in huge number of applications of image processing. Accuracy of image segmentation influence retrieved information for further processing in classification and other task. In image segmentation algorithms, a single segmentation technique is not sufficient in providing accurate segmentation results in many cases. In this paper we are proposing a combining approach of image segmentation techniques for improving segmentation accuracy. As a case study fruit mango is selected for classification based on surface defect. This classification method consists of three steps: (a) image pre-processing, (b) feature extraction and feature selection and (c) classification of mango. Feature extraction phase is performed on an enhanced input image. In feature selection PCA methodology is used. In classification three classifiers BPNN, Naïve bayes and LDA are used. Proposed image segmentation technique is tested on online dataset and our own collected images database. Proposed segmentation technique performance is compared with existing segmentation techniques. Classification results of BPNN in training and testing phase are acceptable for proposed segmentation technique.

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