Hybrid Quantum Error-Corrected Hadamard Edge Detection Dengan Adaptive State-Vector Mean Thresholding Untuk Segmentasi Citra MRI

Rakalangi, Dhanar Agastya (2026) Hybrid Quantum Error-Corrected Hadamard Edge Detection Dengan Adaptive State-Vector Mean Thresholding Untuk Segmentasi Citra MRI. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Segmentasi tumor otak pada citra Magnetic Resonance Imaging (MRI) diperlukan untuk memisahkan area tumor dari jaringan sehat sehingga lokasi dan bentuk lesi dapat dikenali secara lebih objektif. Namun, proses tersebut masih menghadapi kendala karena batas lesi sering memiliki kontras rendah, bentuk yang beragam serta intensitas yang menyerupai jaringan di sekitarnya, sehingga piksel tumor dan non-tumor sulit dibedakan. Permasalahan ini mendorong pemanfaatan informasi sebagai fitur pendukung segmentasi pada citra MRI glioma, meningioma, dan pituitary. Untuk menghasilkan informasi tepi tersebut, digunakan Hybrid Quantum Error-Corrected Hadamard Edge Detection with Adaptive State-Vector Mean Thresholding (HQEHED-AMT), sedangkan klasifikasi piksel
dilakukan menggunakan Particle Swarm Optimization-Support Vector Machine (PSO-SVM). HQEHED-AMT diterapkan melalui pengodean citra menggunakan Quantum Probability Image Encoding (QPIE), pemindaian horizontal dan vertikal, proses AMT, koreksi respons dengan PPQEC, serta penggabungan hasil kedua arah pemindaian. Edge map yang dihasilkan digunakan sebagai salah satu dari sepuluh fitur piksel bersama
fitur intensitas dan karakteristik lokal, sedangkan PSO digunakan untuk menentukan parameter C dan γsvm pada model SVM. Kinerja deteksi tepi dibandingkan dengan QHED
dasar, sementara hasil segmentasi dibandingkan dengan Sobel, Prewitt, dan LoG yang dikombinasikan dengan PSO-SVM. Hasil pengujian menunjukkan bahwa HQEHED-AMT memperoleh nilai Figure of Merit yang lebih tinggi daripada QHED pada ketiga jenis tumor, meskipun rata-ratanya masih lebih rendah dibandingkan beberapa metode deteksi tepi klasik. Kombinasi HQEHED-AMT dan PSO-SVM menghasilkan rata-rata raw mask
dengan Dice sebesar 0,473, IoU sebesar 0,318, dan akurasi sebesar 0,952. Setelah post processing, performanya meningkat menjadi Dice sebesar 0,600, IoU sebesar 0,441, dan
akurasi sebesar 0,970. Hasil tersebut menunjukkan bahwa informasi tepi HQEHED-AMT dapat mendukung segmentasi tumor otak ketika dikombinasikan dengan fitur intensitas dan karakteristik lokal piksel.
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Brain tumor segmentation in Magnetic Resonance Imaging (MRI) is required to separate tumor regions from healthy tissues so that the location and shape of the lesion can be identified more objectively. However, this process still faces challenges because lesion boundaries often exhibit low contrast, diverse shapes, and intensity characteristics
similar to the surrounding tissues, making it difficult to distinguish tumor and non tumor pixels. This problem motivates the use of edge information as a supporting
feature for segmentation in glioma, meningioma, and pituitary MRI images. To generate this edge information, Hybrid Quantum Error-Corrected Hadamard Edge Detection with
Adaptive State-Vector Mean Thresholding (HQEHED–AMT) is employed, whereas pixel classification is performed using Particle Swarm Optimization–Support Vector Machine
(PSO–SVM). HQEHED–AMT is implemented through image encoding using Quantum Probability Image Encoding (QPIE), horizontal and vertical scanning, the Adaptive Mean Thresholding (AMT) process, response correction using Probabilistic Post-Quantum Error Correction (PPQEC), and the fusion of both scanning directions. The resulting edge map is used as one of ten pixel features together with intensity and local characteristic features, while PSO is employed to optimize the SVM parameters C and γsvm. Edge detection performance is compared with the baseline QHED method, whereas segmentation
performance is compared with Sobel, Prewitt, and Laplacian of Gaussian (LoG), each combined with PSO–SVM. The experimental results show that HQEHED–AMT achieves a higher Figure of Merit (FOM) than the baseline QHED method for all three tumor types, although its average FOM remains lower than that of several classical edge detection
methods. The combination of HQEHED–AMT and PSO–SVM produces an average raw mask performance with a Dice score of 0.473, an IoU of 0.318, and an accuracy of 0.952. After post-processing, the performance improves to a Dice score of 0.600, an IoU of 0.441, and an accuracy of 0.970. These results indicate that the edge information generated by
HQEHED–AMT can effectively support brain tumor segmentation when combined with intensity and local pixel characteristic features.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Deteksi Tepi Kuantum, HQEHED–AMT, Segmentasi Tumor Otak, MRI, PSO–SVM, Quantum Edge Detection, HQEHED–AMT, Brain Tumor Segmentation, MRI, PSO–SVM
Subjects:	Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines. Q Science > Q Science (General) > Q337.3 Swarm intelligence R Medicine > RC Internal medicine > RC78.7.N83 Magnetic resonance imaging. T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image processing--Digital techniques. Image analysis--Data processing.
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User:	Dhanar Agastya Rakalangi
Date Deposited:	28 Jul 2026 01:15
Last Modified:	28 Jul 2026 01:15
URI:	http://repository.its.ac.id/id/eprint/136429

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