Medical image retrieval system for diagnosis of brain tumor based on classification and content similarity

Arakeri, Megha P.; Reddy, G. Ram Mohana

doi:10.1109/indcon.2012.6420654

Cited by 5 publications

(7 citation statements)

References 20 publications

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“…Pangkalan data imej di hospitalhospital telah meningkat dengan mendadak seiring dengan penggunaannya semasa latihan harian, kajian perubatan dan pendidikan. Data-data imej ini telah disimpan dan diarkibkan di pangkalan data imej perubatan yang juga dikenali sebagai Sistem Pengarkiban Gambar dan Komunikasi (PACS) (Arakeri and Reddy 2012). Pakar radiologi sentiasa merujuk dan membandingkan kes-kes yang serupa untuk mendapatkan cara rawatan yang sesuai.…”

Section: Perpustakaanunclassified

“…Catatan oleh setiap pakar radiologi adalah berbeza dan tugas ini amat memerihkan jika melibatkan pangkalan data imej yang besar. Oleh itu, kelemahan-kelemahan dalam TBIRS tersebut telah mendorong ahli-ahli kajian memberikan tumpuan kepada CBIRS (Arakeri et al 2012;Madugunki et al 2011).…”

Section: Tbirs Dan Cbirsunclassified

“…JADUAL 1. Kajian yang telah dijalankan oleh ahli penyelidik Kajian Kaedah Modaliti Imej (Arakeri et al 2012) Pengelasan hierarki dan persamaan kandungan MRI (Suganya et al 2012) SVM digabungkan dengan maklumbalas relevan Ultrasound (Nakaram et al 2012) Discrete Wavelet Transform (DWT) X-Ray (Murala and Jonathan Wu 2013) Local ternary co-occurrence patterns MRI dan CT (Arthi et al 2013) CCM (Colour Co-occurrence Matrix) menggunakan peta warna Imej Perubatan Hue Saturation Value (HSV) (Grace et al 2014) Rangka Apache Hadoop Imej Perubatan Berasaskan kaedah graf menggunakan vertex set dan edge set PET-CT (Wan Ahmad et al 2014) Gabor transform, Discrete Wavelet Frame, Grey Level Histogram X-Ray dan kombinasi kaedah ini (Bergamasco and Nunes 2015) Fitur global dan tempatan: Distance Histogram Descriptor, Local Distance MRI Histogram Descriptor, dan 3D Hough Transform Descriptor (Kitanovski et al 2016) Berasaskan hasil pengkuantuman dan pengelasan SVM Imej Perubatan (Sparks and Madabhushi 2016) Out-of-Sample Extrapolation menggunakan Semi-Supervised Manifold Histologi Prostat Learning (OSE-SSL) (Nowaková et al 2017) Pengkuantuman vektor dengan fuzzy S-trees Mammogram (Spanier et al 2017) Hibrid: gabungan dengan penemuan radiologi dari laporan kes CT perubatan dan graf Radlex (Xu et al 2017 Dalam bidang perubatan, imej-imej kebiasaannya disimpan dalam format DICOM. Imej DICOM mengandungi pelbagai maklumat penting mengenai pesakit seperti identiti pesakit, jantina, modaliti imej, bahagian badan dan parameter mesin.…”

Section: Pangkalan Data Imejunclassified

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Content-based Image Retrieval for Medical Application

Zaki¹,

Ahmad²,

Hussain³

et al. 2018

JKUKM

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ABSTRAK Dapatan semula imej (DSI) adalah sistem pencarian imej yang menggunakan ciri-ciri tertentu atau konteks khusus Kata kunci: Imej perubatan; sistem dapatan semula imej; aplikasi perubatan; modaliti x-ray; TBIRS ABSTRACT Image retrieval is an image searching system that uses certain characteristics or context in an image. In a medical field, image retrieval system has been used to provide the needed correct images to the physicians while the diagnosis and treatment process is being conducted. The earlier image retrieval system was a text-based image retrieval system (TBIRS) which using keyword for the image context and it requires human's help to manually make text annotation on the images. This system is still being widely used in the hospitals nowadays. The text annotation process is a laborious task especially when dealing with a huge database and is prone to human errors. To overcome the aforementioned issues, the approach of a content-based image retrieval system (CBIRS) with automatic indexing using visual features such as color

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Section: Perpustakaanunclassified

Section: Tbirs Dan Cbirsunclassified

Section: Pangkalan Data Imejunclassified

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Content-based Image Retrieval for Medical Application

Zaki¹,

Ahmad²,

Hussain³

et al. 2018

JKUKM

View full text Add to dashboard Cite

show abstract

“…However, a limitation of this system is the construction of the global matrix which is prohibitively difficult for large collections. Arakeri et al [12] classify brain tumours based on the rotation invariant shape features (circularity and irregularity) and texture features from tumour region into benign and malignant classes and then use the wavelet based Fourier descriptors and local binary patterns for checking similarity. Shape similarity is calculated using Euclidean distance and the texture similarity is calculated using Chisquare distance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Shape similarity is calculated using Euclidean distance and the texture similarity is calculated using Chisquare distance. Experiments show that this system [12] works better than the earlier developed systems [13][14] [15]. Liu [16] developed a CT lung image retrieval system for assisting differential diagnosis.…”

Section: Literature Reviewmentioning

confidence: 99%