SVM-based texture classification in optical coherence tomography

This paper describes a new method for automated texture classification for glaucoma detection using high resolution retinal Optical Coherence Tomography (OCT). OCT is a non-invasive technique that produces cross-sectional imagery of ocular tissue. Here, we exploit information from OCT im-ages, specifically the inner retinal layer thickness and speckle patterns, to detect glaucoma. The proposed method relies on support vector machines (SVM), while principal component analysis (PCA) is also employed to improve classification performance. Results show that texture features can improve classification accuracy over what is achieved using only layer thickness as existing methods currently do. Index Terms — classification, support vector machine, optical coherence tomography, texture 1

A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

Purpose: To develop a deep learning approach to de-noise optical coherence tomography (OCT) B-scans of the optic nerve head (ONH). Methods: Volume scans consisting of 97 horizontal B-scans were acquired through the center of the ONH using a commercial OCT device (Spectralis) for both eyes of 20 subjects. For each eye, single-frame (without signal averaging), and multi-frame (75x signal averaging) volume scans were obtained. A custom deep learning network was then designed and trained with 2,328 "clean B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance of the de-noising algorithm was assessed qualitatively, and quantitatively on 1,552 B-scans using the signal to noise ratio (SNR), contrast to noise ratio (CNR), and mean structural similarity index metrics (MSSIM). Results: The proposed algorithm successfully denoised unseen single-frame OCT B-scans. The denoised B-scans were qualitatively similar to their corresponding multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean SNR increased from $4.02 \pm 0.68$ dB (single-frame) to $8.14 \pm 1.03$ dB (denoised). For all the ONH tissues, the mean CNR increased from $3.50 \pm 0.56$ (single-frame) to $7.63 \pm 1.81$ (denoised). The MSSIM increased from $0.13 \pm 0.02$ (single frame) to $0.65 \pm 0.03$ (denoised) when compared with the corresponding multi-frame B-scans. Conclusions: Our deep learning algorithm can denoise a single-frame OCT B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior quality OCT B-scans with reduced scanning times and minimal patient discomfort

IVUS-based histology of atherosclerotic plaques: improving longitudinal resolution

Although Virtual Histology (VH) is the in-vivo gold standard for atherosclerosis plaque characterization in IVUS images, it suffers from a poor longitudinal resolution due to ECG-gating. In this paper, we propose an image- based approach to overcome this limitation. Since each tissue have different echogenic characteristics, they show in IVUS images different local frequency components. By using Redundant Wavelet Packet Transform (RWPT), IVUS images are decomposed in multiple sub-band images. To encode the textural statistics of each resulting image, run-length features are extracted from the neighborhood centered on each pixel. To provide the best discrimination power according to these features, relevant sub-bands are selected by using Local Discriminant Bases (LDB) algorithm in combination with Fisher’s criterion. A structure of weighted multi-class SVM permits the classification of the extracted feature vectors into three tissue classes, namely fibro-fatty, necrotic core and dense calcified tissues. Results shows the superiority of our approach with an overall accuracy of 72% in comparison to methods based on Local Binary Pattern and Co-occurrence, which respectively give accuracy rates of 70% and 71%

Improved Stroke Detection at Early Stages Using Haar Wavelets and Laplacian Pyramid

Stroke merupakan pembunuh nomor tiga di dunia, namun hanya sedikit metode tentang deteksi dini. Oleh karena itu dibutuhkan metode untuk mendeteksi hal tersebut. Penelitian ini mengusulkan sebuah metode gabungan untuk mendeteksi dua jenis stroke secara simultan. Haar wavelets untuk mendeteksi stroke hemoragik dan Laplacian pyramid untuk mendeteksi stroke iskemik. Tahapan dalam penelitian ini terdiri dari pra proses tahap 1 dan 2, Haar wavelets, Laplacian pyramid, dan perbaikan kualitas citra. Pra proses adalah menghilangkan bagian tulang tengkorak, reduksi derau, perbaikan kontras, dan menghilangkan bagian selain citra otak. Kemudian dilakukan perbaikan citra. Selanjutnya Haar wavelet digunakan untuk ekstraksi daerah hemoragik sedangkan Laplacian pyramid untuk ekstraksi daerah iskemik. Tahapan terakhir adalah menghitung fitur Grey Level Cooccurrence Matrix (GLCM) sebagai fitur untuk proses klasifikasi. Hasil visualisasi diproses lanjut untuk ekstrasi fitur menggunakan GLCM dengan 12 fitur dan kemudian GLCM dengan 4 fitur. Untuk proses klasifikasi digunakan SVM dan KNN, sedangkan pengukuran performa menggunakan akurasi. Jumlah data hemoragik dan iskemik adalah 45 citra yang dibagi menjadi 2 bagian, 28 citra untuk pengujian dan 17 citra untuk pelatihan. Hasil akhir menunjukkan akurasi tertinggi yang dicapai menggunakan SVM adalah 82% dan KNN adalah 88%