Mina, Luqman Mahmood (2016) Development of A Discrete Wavelet Transform and Artificial Neural Network Based Classification System for Mammogram Images. PhD thesis, Universiti Sains Malaysia.
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Abstract
Pada masa ini, terdapat pelbagai sistem diagnosis bantuan komputer (CAD) yang dibangunkan sejak beberapa tahun lalu untuk membantu ahli radiologi dalam pengecaman lesi mamografi yang boleh menunjukkan kehadiran kanser payudara. Walau bagaimanapun, prestasi CAD terhad oleh dua isu utama iaitu (i) kawasan yang tidak diingini (seperti label segi empat tepat berintensiti tinggi, pita, artifak, antara muka kulit dan air, dan lain-lain) yang boleh mengganggu pengecaman kanser payudara dan mengurangkan kadar ketepatan CAD, (ii) ketidakteraturan tekstur mamogram yang meliputi ciri-ciri seperti entropi, tenaga, kepencongan, kurtosis, min dan sisihan piawai yang berhubung kait dalam domain ruang dan tidak penting untuk pengelasan. Oleh itu, bagi menangani masalah yang dinyatakan di atas, sistem CAD yang lebih baik untuk imej mamogram dicadangkan. CAD yang dicadangkan ini terdiri daripada tiga peringkat utama, iaitu prapemprosesan, pengekstrakan ciri dan pengelasan imej mamogram. Pada peringkat prapemprosesan, Adaptive Multilevel Threshold (AMLT), yang berjaya menyingkirkan kawasan yang tidak diingini seperti yang dinyatakan sebelum ini, dicadangkan. Hal ini memberikan kelebihan kepada sistem dengan membolehkan pencarian terhadap keabnormalan terkekang pada lingkungan tisu payudara tanpa menjejaskan kawasan yang tidak diingini dalam latar belakang imej. Pada peringkat pengekstrakan ciri, dua ciri baharu iaitu median maksimum dan minimum subjalur berfrekuensi tinggi dicadangkan untuk pengkelasan imej mamogram kepada kategori normal, benigna dan malignan. Analisis plot kotak membuktikan bahawa kedua-dua ciri baharu tiada hubung kait dan penting untuk pengelasan imej mamogram berbanding dengan ciri-ciri konvensional. Pada peringkat pengelasan, rangkaian perseptron berbilang lapis (MLP) digunakan untuk mengelaskan mamogram normal dan tidak normal pada fasa pertama dan mamogram benigna dan malignan pada fasa kedua. Keputusan purata yang terhasil daripada 322 imej mamogram pada fasa pertama merumuskan bahawa pendekatan yang dicadangkan berjaya mencapai keputusan yang boleh harap dengan ketepatan sebanyak 96,27%, kepekaan sebanyak 94,78% dan kekhususan sebanyak 96.60%. Di samping itu, keputusan purata yang terhasil daripada 115 imej yang tidak normal mempunyai ketepatan, kepekaan dan kekhususan, masing-masing sebanyak 95.65%, 96.18% dan 95.38%. Keputusan eksperimen akhir menunjukkan bahawa sistem pengelasan mamogram yang dibangunkan mampu mencapai pengelasan tertinggi berbanding dengan sistem terkini yang lain. Prestasi pengelasan yang menggalakkan ini menunjukkan bahawa sistem yang dicadangkan tersebut boleh digunakan untuk membantu ahli patologi dalam menjalankan proses diagnosis. ________________________________________________________________________________________________________________________ Nowadays, numerous computer-aided diagnosis (CAD) systems have been developed to assist radiologists in the recognition of mammographic lesions that may indicate the presence of breast cancer. However, the performance of CAD is limited by two main issues; (i) unwanted regions (i.e. high-intensity rectangular label, tape, artefact, skin-air interface, etc.) could disturb the detection of breast cancer and reduce the accuracy rate of CAD, (ii) the irregularity of mammograms’ texture in which features such as entropy, energy, skewness, kurtosis, mean, and standard deviation are correlated in the spatial domain and insignificant for classification. Therefore, to address the aforementioned problems, an improved CAD system for the mammogram image is proposed. The proposed CAD consists of three main stages, namely pre-processing, feature extraction, and classification of mammogram images. In pre-processing step, Adaptive Multilevel Threshold (AMLT) is proposed, which successfully removes the above-mentioned unwanted regions. It gives the advantage to the system where it allows the search for abnormalities to be constrained to the region of the breast tissue without the effect of the unwanted regions in the image background. In feature extraction stage, two new features, namely medians of maximum and minimum of high-frequency subbands have been proposed to classify the mammogram images into normal, benign and malignant. Box plot analysis has proven that both new features are uncorrelated and significant for classification of mammogram images as compared to the conventional features. In the classification stage, multilayer perceptron (MLP) network is employed to classify normal and abnormal mammograms in the first phase and benign and malignant in the second phase. The average results produced from 322 mammogram images in the first phase concluded that the proposed approach attained reliable results with an accuracy of 96.27%, sensitivity of 94.78% and specificity of 96.60%. In addition, the average results produced from 115 abnormal images for accuracy, sensitivity, and specificity are 95.65%, 96.18%, and 95.38% respectively. The final experimental results show that the developed mammogram classification system is able to achieve the highest classification as compared to the other state-of-the-art systems. These promising classification performances show that the proposed system could probably be used to assist pathologists in their diagnosis process.
| Item Type: | Thesis (PhD) |
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| Additional Information: | Full text is available at http://irplus.eng.usm.my:8080/ir_plus/institutionalPublicationPublicView.action?institutionalItemId=2115 |
| Subjects: | T Technology T Technology > TK Electrical Engineering. Electronics. Nuclear Engineering > TK7800-8360 Electronics |
| Divisions: | Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraaan Elektrik & Elektronik (School of Electrical & Electronic Engineering) > Thesis |
| Depositing User: | Mr Mohd Jasnizam Mohd Salleh |
| Date Deposited: | 13 Jun 2018 02:26 |
| Last Modified: | 13 Jun 2018 02:26 |
| URI: | http://eprints.usm.my/id/eprint/40775 |
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