A Review of Medical Image Segmentation Algorithms

Ramesh, K.; Kumar, Gaurav; Swapna, K.; Datta, Debabrata; Rajest, S Dr Suman

doi:10.4108/eai.12-4-2021.169184

Cited by 138 publications

(39 citation statements)

References 45 publications

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“…To overcome this, it is necessary to reconstruct the gradient image morphology using opening, closing, dilation, and erosion. This process is to overcome oversegmentation and noise problems, as well as its reconstruction to eliminate extreme local areas, due to noise that approaches the minimum and/or maximum areas (Dorgham, 2018;Ramesh et al, 2021). In the opening process, it is intended to be able to remove smaller soil grain objects through erosion and smooth the boundaries of objects in the image without changing the area of the object due to dilation.…”

Section: Resultsmentioning

confidence: 99%

Sand Soil Image Processing Using the Watershed Transform and Otsu Thresholding Based on Gaussian Noise

Tomasila

2022

jinav j. inf. vis.

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Image processing technology is one of the technologies that can help facilitate and speed up human work, especially in the process of determining the grain size distribution of soil in a civil building plan. Its utilization has been widely used to study, analyze, and understand the structure and framework of the soil. Image analysis is carried out as an initial or fundamental step in image processing to discover and comprehend information. With so many image segmentation methods, it is necessary to conduct research to determine which method is best for sandy soil image segmentation based on one of the image segmentation quality criteria, namely gaussian image noise. By testing the watershed transform method and the Otsu thresholding method as two of the area-based methods that are considered suitable for segmenting sandy soil images before and after distorted Gaussian noise based on the calculation of the mean square error (MSE) value.The results showed that the watershed transform method is better for segmenting sandy soil images when compared to the Otsu thresholding method. This is indicated by the average squared error (mse) of 3.08 for the watershed transform method and 4.09 for the Otsu Thresholding method. In addition to the comparison of quality tests of sandy soil based on gaussian noise with standard deviation values of normal distribution and noise intensities of 10, 20, and 30, it proves that the watershed transform method is still better at segmenting noise-distorted sandy soil images than the Otsu thresholding method. However, in terms of processing time, the Otsu Thresholding method is faster or better than the Watershed Transform method. of the results or conclusions brief. There are no citations, tables or figures in abstract.

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Section: Resultsmentioning

confidence: 99%

Sand Soil Image Processing Using the Watershed Transform and Otsu Thresholding Based on Gaussian Noise

Tomasila

2022

jinav j. inf. vis.

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“…Image segmentation involves partitioning pixels in a digital image into coherent portions called homogeneous segments [43]. Pixels in the same segment share qualities such as resholding is considered the simplest method with two variants.…”

Section: Image Segmentationmentioning

confidence: 99%

A Review on Thermal Imaging-Based Breast Cancer Detection Using Deep Learning

Tsietso

Yahya

Samikannu

2022

Mobile Information Systems

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Breast cancer is the most common form of cancer in women. Its aggressive nature has made it one of the chief factors of high female mortality. Therefore, this has motivated research to achieve early diagnosis since it is the best strategy for patient survival. Currently, mammography is the gold standard for detecting breast cancer. However, it is expensive, unsuitable for dense breasts, and an invasive process that exposes the patient to radiation. Infrared thermography is gaining popularity as a screening modality for the early detection of breast cancer. It is a noninvasive and cost-effective modality that allows health practitioners to observe the temperature profile of the breast region for signs of cancerous tumors. Deep learning has emerged as a powerful computational tool for the early detection of breast cancer in radiology. As such, this study presents a review that shows existing work on deep learning-based Computer-aided Diagnosis (CADx) systems for breast cancer detection. In the same context, it reflects on classification utilizing breast thermograms. It first provides an overview of infrared thermography, details on available breast thermogram datasets, and then segmentation techniques applied to these thermograms. We also provide a brief overview of deep neural networks. Finally, it reviews works adopting Deep Neural Networks (DNNs) for breast thermogram classification.

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“…The proposed model is experimented with using MATLAB simulation tool running under 4GHz processor, 8GB RAM and 64-bit Windows 8 operating system [51][52][53][54]. The performance of the proposed model is computed in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) and compared with the existing techniques [55][56][57][58].…”

Section: Performance Measurementmentioning

confidence: 99%

EM_GA-RS: Expectation Maximization and GA-based Movie Recommender System

Asha

Rajkumar

2022

EAI Endorsed Scal Inf Syst

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This work introduced a novel approach for the movie recommender system using a machine learning approach. This work introduces a clustering-based approach to introduce a recommender system (RS). The conventional clustering approaches suffer from the clustering error issue, which leads to degraded performance. Hence, to overcome this issue, we developed an expectation- maximization-based clustering approach. However, due to imbalanced data, the performance of RS is degraded due to multicollinearity issues. Hence, we Incorporate PCA (Principal Component Analysis) based dimensionality reduction model to improve the performance. Finally, we aim to reduce the error; thus, a Genetic Algorithm (GA) is included to achieve the optimal clusters and assign the suitable recommendation. The experimental study is carried out on publically available movie datasets performance of the proposed approach is measured in terms of MSE (Mean Squared Error) and Root Mean Squared Error (RMSE). The comparative study shows that the proposed approach achieves better performance when compared with a state-of-art movie recommendation system.

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A Review of Medical Image Segmentation Algorithms

Cited by 138 publications

References 45 publications

Sand Soil Image Processing Using the Watershed Transform and Otsu Thresholding Based on Gaussian Noise

Sand Soil Image Processing Using the Watershed Transform and Otsu Thresholding Based on Gaussian Noise

A Review on Thermal Imaging-Based Breast Cancer Detection Using Deep Learning

EM_GA-RS: Expectation Maximization and GA-based Movie Recommender System

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