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Abstrak :
This volume mainly contains information on the diagnosis, therapy, and prognosis of brain tumors. Insights on the understanding of molecular pathways involved in tumor biology are explained, which should lead to the development of effective drugs. Information on pathways (e.g., hedgehog) facilitates targeted therapies in cancer. Tumor models are also presented, which utilize expression data, pathway sensitivity, and genetic abnormalities, representing targets in cancer. For example, rat model of malignant brain tumors using implantation of doxorubicin with drug eluting beads for delivery is explained. The future of pathway-driven therapies for tumors is summarized. The importance of personalizing cancer care is emphasized. The need for supportive measures for survivors of brain cancer is pointed out, so is the quality of life monitoring. The need of rehabilitation therapy for patients with primary and metastatic brain tumors is also emphasized. Role of MicroRNA in distinguishing primary tumors from metastatic primary tumors is discussed. Advantages and limitations of chemotherapy (e.g., temozolomide and doxorubicin) are discussed. The complexity of tumor to tumor transfer is explained; examples discussed are: brain metastases from breast cancer and brain metastases fro non-small cell lung carcinoma. Identification and characterization of biomarkers, including those for metastatic brain tumors, are presented. Genomic analysis for identifying clinically relevant subtypes of glioblastoma is included. A large number of imaging modalities are detailed to study progression and invasion of gliomas.

Abstrak :
ABSTRAK
Peningkatan kualitas citra medis khususnya untuk bagian kepala manusia terus dikembangkan, termasuk dengan pemodelan 3D. Hal ini dilakukan untuk mengurangi kesalahan dalam proses diagnosa dan memfasilitasi pendeteksian tumor otak dengan pendekatan 3D. Dalam prosesnya, citra MRI otak dianalisa secara 3D sehingga diperoleh bagian tumor otak. Citra MRI dikonversi dari citra berformat MINC. Citra diklasifikasi untuk mendeteksi objek menggunakan K-Means Clustering yang akan memisahkan bagian tumor dan otak. Proses filter dilakukan menggunakan Non-Local Means sehingga noise hasil pengolahan dapat berkurang dari proses sebelumnya. Hasil citra pengolahan disegmentasi untuk meningkatkan dan mendukung proses rekonstruksi menggunakan Thresholding. Terakhir adalah merekonstruksi citra dalam bentuk 3D menggunakan metode Marching Cube. Evaluasi akurasi sistem meliputi pengurangan resolusi, pengujian citra normal, uji perbandingan, penggantian format citra dan penambahan noise. Hasil akurasi pendeteksian tumor otak mencapai 100% untuk format PNG dan resolusi 512x512, 97,7% untuk resolusi 256x256, 96,9% untuk citra normal tanpa tumor dan 97,96% berdasarkan perbandingan data olah dengan data referensi. Format PNG memiliki akurasi dibandingkan format JPEG dengan perbedaan sebesar 4%. Pengujian dengan menambahkan noise menghasilkan akurasi 87,6% untuk densitas 0,01, 83,6% untuk 0,05 dan 74,5% untuk 0,09.

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ABSTRACT
Medical image enhancement especially for human brain imageries is rapidly developed, including 3D modeling. This research is aimed to reduce the error of diagnosis process and facilitate brain tumor detection using 3D approach. In the process, 3D brain from MRI imageries is analyzed to detect brain tumors. MRI image is converted from MINC format. Then, the image is classified to detect objects using K-Means Clustering to divide each part of brain. Filtering is performed using Non-Local Means to remove noise from previous processes. The result of imageries are segmented to enhance and support reconstruction process using Thresholding. Finally, 3D image reconstruction is performed using Marching Cube method. The accuracy of brain tumor detection is evaluated of resolution reduction, non tumor image testing, comparison testing, modifying image format, and adding noise. The accuracy rate of brain tumor detection is 100% for PNG format and 512x512 resolution, 97,7% for 256x256 resolution, 96,9% for non tumor image and 97,96% for comparison between ideal image and reference data. PNG format has better accuracy with JPEG by 4% improvement. The accuracy of adding noise is 87,6% for 0,01 density, 83,6% for 0,05 and 74,5% for 0,09.