Big Data in Medical Image Processing

Suganya, R.; Rajaram, S.; Abdullah, A. Sheik

doi:10.1201/b22456

Cited by 10 publications

(4 citation statements)

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“…Texture features in this research were calculated from the resulting GLCM, such as ASM or energy, contrast, correlation, IDM or homogeneity, and entropy. The extraction features of texture characteristics are as follows [25]- [27].…”

Section: Feature Extraction Of the Gray-level Co-occurrence Matrixmentioning

confidence: 99%

Klasifikasi Penyakit Daun Kopi Robusta Menggunakan Metode SVM dengan Ekstraksi Ciri GLCM

Agus Supriyanto,

R. Rizal Isnanto,

Oky Dwi Nurhayati

2023

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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Tanaman kopi merupakan sumber pendapatan bagi beberapa petani di Indonesia dan berperan penting sebagai penghasil devisa negara. Produksi tanaman kopi dapat menurun akibat serangan hama dan penyakit. Beberapa penyakit yang sering terjadi pada tanaman kopi adalah penyakit pada daun, berupa bercak daun (Cercospora coffeicola) dan karat daun (Hemileia vastatrix). Penelitian yang dilakukan bertujuan untuk mengenali jenis penyakit pada daun kopi robusta dan mengetahui hasil klasifikasi. Penerapan teknologi pengolahan citra berbasis machine learning menggunakan metode klasifikasi support vector machine (SVM) berdasarkan ekstraksi ciri gray level co-occurrence matrix (GLCM) menjadi solusi yang diusulkan. Prapengolahan diperlukan sebelum dilakukannya pengolahan untuk meningkatkan kualitas citra supaya mudah dianalisis. Kemudian, dilakukan segmentasi menggunakan k-means clustering. Proses segmentasi k-means clustering dengan tiga cluster digunakan untuk membedakan bagian yang terdampak penyakit bercak daun atau karat daun dengan bagian yang tidak terdampak penyakit. Metode GLCM digunakan sebagai ekstraksi ciri, berdasarkan fitur angular second moment (ASM) atau energi, kontras, korelasi, inverse different moment (IDM) atau homogenitas, dan entropi dengan sudut 0°, 45°, 90°, dan 135°, serta jarak antar piksel 1 sampai 3 piksel. Metode SVM digunakan sebagai klasifikasi dengan kernel linear, polinomial, dan Radial Basis Function (RBF) Gaussian. Penelitian ini menggunakan citra bercak daun dan karat daun, dengan jumlah data latih 320 citra dan data uji 80 citra. Hasil pengujian terbaik diperoleh pada kernel RBF Gaussian dengan tingkat akurasi terbaik sebesar 97,5%, presisi 95,24%, recall 100%, dan F1-score 97,56%.

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Section: Feature Extraction Of the Gray-level Co-occurrence Matrixmentioning

confidence: 99%

Klasifikasi Penyakit Daun Kopi Robusta Menggunakan Metode SVM dengan Ekstraksi Ciri GLCM

Agus Supriyanto,

R. Rizal Isnanto,

Oky Dwi Nurhayati

2023

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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“…The data is distributed and accessible by different cores and each core processes the data subject to its locality and affinity. [3] A. DESIGN GOALS…”

Section: Design Goals and Decisionsmentioning

confidence: 99%

“…The associate editor coordinating the review of this manuscript and approving it for publication was Yinliang Xu . so, data is initially distributed and processed by different cores based on locality and affinity [3]. Operating systems used in such environments are Linux, FreeBSD as well as other UNIX-based systems.…”

Section: Introductionmentioning

confidence: 99%

NileOS: A Distributed Asymmetric Core-Based Micro-Kernel for Big Data Processing

et al. 2021

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Big Data applications have demanding expectations on computational resources front. Thus, general purpose operating systems are not a good fit. In this paper, we present a new special purpose distributed micro-kernel designed with big data applications' needs in mind. The new micro-kernel adopts a core-based Asymmetric Multiprocessing (AMP) approach. It optimizes interrupt management and I/O to suit the Map-Reduce model. The proposed micro-kernel design is based on Inter-processor Interrupts over Ethernet (IPIoE) frames and a BareMetal Operating System Markup Language (BOSML). A transparent deployment mechanism is presented to completely shield the developer of the micro-kernel service from the underlying distribution infrastructure and decouple the application implementation from its deployment perspective. Based on the initial prototype and the experiments presented, a considerable gain in performance of average 2.34 folds was achieved using the distributed TeraSort benchmark over Linux/Hadoop.

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“…However, the tolerance or adaptability of these techniques are limited, which then complicates the analysis stage. They are also quite sensitive to the choice and fine tuning of various parameters [36, 37].…”

Section: Introductionmentioning

confidence: 99%

Automatic Annotation of Retinal Layers in Optical Coherence Tomography Images

Dodo

Eltayef

et al. 2019

J Med Syst

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Early diagnosis of retinal OCT images has been shown to curtail blindness and visual impairments. However, the advancement of ophthalmic imaging technologies produces an ever-growing scale of retina images, both in volume and variety, which overwhelms the ophthalmologist ability to segment these images. While many automated methods exist, speckle noise and intensity inhomogeneity negatively impacts the performance of these methods. We present a comprehensive and fully automatic method for annotation of retinal layers in OCT images comprising of fuzzy histogram hyperbolisation (FHH) and graph cut methods to segment 7 retinal layers across 8 boundaries. The FHH handles speckle noise and inhomogeneity in the preprocessing step. Then the normalised vertical image gradient, and it’s inverse to represent image intensity in calculating two adjacency matrices and then the FHH reassigns the edge-weights to make edges along retinal boundaries have a low cost, and graph cut method identifies the shortest-paths (layer boundaries). The method is evaluated on 150 B-Scan images, 50 each from the temporal, foveal and nasal regions were used in our study. Promising experimental results have been achieved with high tolerance and adaptability to contour variance and pathological inconsistency of the retinal layers in all (temporal, foveal and nasal) regions. The method also achieves high accuracy, sensitivity, and Dice score of 0.98360, 0.9692 and 0.9712, respectively in segmenting the retinal nerve fibre layer. The annotation can facilitate eye examination by providing accurate results. The integration of the vertical gradients into the graph cut framework, which captures the unique characteristics of retinal structures, is particularly useful in finding the actual minimum paths across multiple retinal layer boundaries. Prior knowledge plays an integral role in image segmentation.

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Big Data in Medical Image Processing

Cited by 10 publications

References 0 publications

Klasifikasi Penyakit Daun Kopi Robusta Menggunakan Metode SVM dengan Ekstraksi Ciri GLCM

Klasifikasi Penyakit Daun Kopi Robusta Menggunakan Metode SVM dengan Ekstraksi Ciri GLCM

NileOS: A Distributed Asymmetric Core-Based Micro-Kernel for Big Data Processing

Automatic Annotation of Retinal Layers in Optical Coherence Tomography Images

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