Development Of Techniques For The Detection Of Tumours In Breast Magnetic Resonance Imaging
Kanser payudara ialah penyebab utama kematian di kalangan pesakit kanser yang melanda wanita dan kanser kedua paling lazim di seluruh dunia. Pengimejan Resonans Magnetik (MRI) adalah salah satu daripada alat-alat radiologi yang paling berkesan untuk menyaring kanser payudara. Bagaimanapun, teknik-te...
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| Format: | Thesis |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://eprints.usm.my/41667/1/Development_Of_Techniques_For_The_Detection_Of_Tumours_In_Breast_Magnetic_Resonance_Imaging.pdf |
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| Summary: | Kanser payudara ialah penyebab utama kematian di kalangan pesakit kanser yang melanda wanita dan kanser kedua paling lazim di seluruh dunia. Pengimejan Resonans Magnetik (MRI) adalah salah satu daripada alat-alat radiologi yang paling berkesan untuk menyaring kanser payudara. Bagaimanapun, teknik-teknik pemprosesan imej diperlukan bagi membantu pakar radiologi dalam mentafsir imej dan memisahkan wilayah tumor bagi mengurangkan jumlah positif yang palsu. Dalam kajian ini, pendekatan segmentasi dengan ciri-ciri automatik dibangunkan untuk tumor MRI payudara. Kaedah bermula dengan pemerolehan data diikuti oleh proses prapemprosesan. Ini diikuti dengan proses pengecualian garis kulit payudara menggunakan kaedah bersepadu Level Set Active Contour and Morphological Thinning. Berikutnya, kesan penting dikesan menggunakan kaedah Mean Maximum Raw Thresholding (MMRT) dicadangkan. Kemudian, pada fasa segmentasi tumor, dua kaedah diubahsuai Seeded Region Growing (SRG) dicadangkan; iaitu Breast MRI Tumour menggunakan Modified Automatic SRG (BMRI-MASRG) dan Breast MRI Tumour menggunakan SRG berdasarkan Particle Swarm Optimization Image Clustering (BMRI-SRGPSOC). Data set MRI payudara RIDER digunakan untuk penilaian dan keputusan dibandingkan dengan data set sebenar (ground truth). Daripada analisis keputusan, dapat diperhatikan bahawa pendekatan yang dicadangkan mencatat hasil-hasil hasilan yang tinggi menerusi pelbagai langkah. Keputusan pengecualian garis kulit mencatat purata prestasi yang tinggi bagi kedua-dua peringkat peringkat segmentasi sempadan (kepekaan = 0.81 dan ketentuan = 0.94 dan peringkat penyingkiran kawasan kulit (kepekaan = 0.86 dan ketentuan = 0.97). Penilaian kualiti MMRT menunjuk keputusan lebih jitu dengan purata PSNR = 69.97 dan MSE = 0.01. Dalam fasa segmentasi tumor, keputusan-keputusan kepekaan untuk dua kaedah yang dicadangkan; BMRI-MASRG dan BMRI-SRGPSOC, menunjukkan hasil segmentasi yang lebih tepat dengan purata masing-masingnya 0.82 dan 0.84. Begitu juga, hasil ketentuan mencatat prestasi lebih baik berbanding dengan cara sebelumnya. Purata BMRI-MASRG dan BMRI-SRGPSOC adalah masing-masingnya 0.90 dan 0.91.
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Breast cancer is the leading cause of death amongst cancer patients afflicting women and the second most common cancer around the world. Magnetic Resonance Imaging (MRI) is one of the most effective radiology tools to screen breast cancer. However, image processing techniques are needed to help radiologists in interpreting the images and segmenting tumours regions to reduce the number of false-positive. In this study, a segmentation approach with automatic features is developed for breast MRI tumours. The methodology starts with data acquisition followed by pre-processing. This is then followed with breast skin-line exclusion using integrated method of Level Set Active Contour and Morphological Thinning. Next, regions of interests are detected using proposed Mean Maximum Raw Thresholding method (MMRT). In the tumour segmentation phase, two modified Seeded Region Growing (SRG) methods are proposed; i.e. Breast MRI Tumour using Modified Automatic SRG (BMRI-MASRG) and Breast MRI Tumour using SRG based on Particle Swarm Optimization Image Clustering (BMRI-SRGPSOC). The RIDER breast MRI dataset was used for evaluation and the results are compared with the ground truth of the dataset. From analysing the evaluation results, it can be noticed that the proposed approaches scored high results using various measures comparing to previous methods. The results of skin-line exclusion scored high average performance in both stages; border segmentation stage (sensitivity = 0.81 and specificity = 0.94) and removal stage (sensitivity = 0.86 and specificity = 0.97). The quality evaluation of MMRT showed improved results with average of PSNR = 69.97 and MSE = 0.01. In the tumour segmentation phase, the sensitivity results of the two proposed methods; BMRI-MASRG and BMRI-SRGPSOC showed more accurate segmentation with averages of 0.82 and 0.84 respectively. Similarly, the specificity results also scored better performance compared to previous methods. The averages of BMRI-MASRG and BMRI-SRGPSOC are 0.90 and 0.91 respectively.
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