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Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

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Evaporation-reducing Culture Condition Increases the Reproducibility of Multicellular Spheroid Formation in Microtiter Plates

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Applying an adaptive Otsu-based initialization algorithm to optimize active contour models for skin lesion

Yushaa Shafqat Malik¹, Maria Tamoor¹, Asma Naseer²

¹Department of Computer Science, Forman Christian College University, Lahore, Pakistan.

Journal of X-Ray Science and Technology

|September 12, 2022

Summary

This summary is machine-generated.

A new adaptive Otsu-based initialization (AOI) method automates contour placement for active contour model (ACM) segmentation. This approach significantly improves medical image analysis for computer-aided diagnosis (CAD) systems.

Keywords:

Active contour image segmentation region of interest (ROI)skin lesion

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Area of Science:

Medical image processing
Computer-aided diagnosis (CAD)
Biomedical engineering

Background:

Medical image segmentation is crucial for computer-aided diagnosis (CAD).
Active contour models (ACM) are widely used for region of interest (ROI) segmentation.
ACM performance is highly dependent on initial contour placement.

Purpose of the Study:

To develop a fully automated initialization process for ACM.
To enhance the effectiveness and accuracy of ROI segmentation in medical images.
To investigate the feasibility of an optimal automated initialization for ACM.

Main Methods:

Proposed an adaptive Otsu-based initialization (AOI) algorithm for automated contour generation.
Utilized ACM to refine the initial contour produced by AOI for accurate segmentation.
Evaluated the algorithm on the ISIC-2017 Skin Lesion dataset.

Main Results:

The AOI-initialized ACM significantly outperformed Otsu thresholding (p≤0.05).
Achieved a Dice Score Coefficient (DSC) of 0.88 and Jaccard Index (JI) of 0.79.
Demonstrated computational complexity of O(mn).

Conclusions:

The proposed AOI method offers superior performance compared to other skin lesion segmentation techniques.
The AOI method requires no training time, enhancing its efficiency over machine learning and deep learning approaches.
This automated initialization significantly improves ACM-based medical image segmentation.