Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan
Nadira Eka Putri, Nadira Eka Putri
Unknown Affiliation
Computer Science & IT Control & Systems Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Studi Komparasi HE dan CLAHE pada Image Enhancement untuk Segmentasi Citra dalam Deteksi Tumor pada Otak Nadira Eka Putri, Nadira Eka Putri; Reni Rahmadewi , Reni Rahmadewi; Salsanabila Mariestiara Putri , Salsanabila Mariestiara Putri
Telekontran : Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan Vol. 13 No. 2 (2025): TELEKONTRAN vol 13 no 2 Oktober 2025
Publisher : Program Studi Teknik Elektro, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/telekontran.v13i2.18163

Abstract

Abstract - Accurate segmentation of brain tumor images plays a crucial role in supporting precise diagnosis and treatment planning. Brain tumor image segmentation requires an effective image enhancement process to distinguish healthy and abnormal tissues. While deep learning methods are advancing, Histogram Equalization (HE) and Contrast-Limited Adaptive Histogram Equalization (CLAHE) techniques remain essential for lightweight and efficient preprocessing. The dataset consists of 100 brain MRI images, including 50 normal and 50 tumor cases. The evaluation employs quantitative metrics including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Square Error (MSE), and processing time. Experimental results indicate that HE achieves an accuracy of 85% (PSNR = 18.4 dB, SSIM = 0.71, MSE = 0.015, average processing time = 0.86 s), while CLAHE achieves 95% accuracy (PSNR = 24.9 dB, SSIM = 0.89, MSE = 0.009, average processing time = 1.12 s). These findings indicate that CLAHE provides clearer and more detailed image improvements compared to HE. Keywords: Brain Tumor, Preprocessing, Histogram Equalization (HE), Contrast-Limited Adaptive Histogram Equalization (CLAHE)
Co-Authors Reni Rahmadewi , Reni Rahmadewi Salsanabila Mariestiara Putri , Salsanabila Mariestiara Putri