Behcet Ugur Toreyin

DeepMQ: A Deep Learning Approach Based Myelin Quantification in Microscopic Fluorescence Images

Oligodendrocytes wrap around the axons and form the myelin. Myelin facilitates rapid neural signal transmission. Any damage to myelin disrupts neuronal communication leading to neurological diseases such as multiple sclerosis (MS). There is no cure for MS. This is, in part, due to lack of an efficient method for myelin quantification during drug screening. In this study, an image analysis based myelin sheath detection method, DeepMQ, is developed. The method consists of a feature extraction step followed by a deep learning based binary classification module. The images, which were acquired on a confocal microscope contain three channels and multiple z-sections. Each channel represents either oligodendroyctes, neurons, or nuclei. During feature extraction, 26-neighbours of each voxel is mapped onto a 2D feature image. This image is, then, fed to the deep learning classifier, in order to detect myelin. Results indicate that 93.38% accuracy is achieved in a set of fluorescence microscope images of mouse stem cell-derived oligodendroyctes and neurons. To the best of authors’ knowledge, this is the first study utilizing image analysis along with machine learning techniques to quantify myelination.

Segmentation of precursor lesions in cervical cancer using convolutional neural networks

Cervical carcinoma is one of the frequently seen cancers in the world and in our country, develops from precursor lesions. These precursor lesions are analyzed by pathologists so that the diagnosis of the disease can be made. In this study, a system that performs automatic detection of pre-cancerous lesions was performed using the convolutional neural networks (CNNs). In the training phase, lesion recognition performance of the proposed system has reached 92%. Thereafter, whole image was segmented by using 60 × 60 pixel tiles during the training phase. After all, the precursor lesions were segmented with 81.71% Dice coefficient.

Lossy compression of hyperspectral images by using Enhanced Multivariance Products Representation (EMPR) method

In preceding paper, a compression algorithm for hyperspectral images using a novel multivariate data decomposition method called Enhanced Multivariance Products Representation (EMPR) is developed. The test results obtained by performing some EMPR approximations of different orders and their qualities are reported. In order to improve performance, EMPR approach is applied to high-subband of hyperspectral data which is spectrally decorrelated using Haar wavelet transform. Low subbands are losslessly compressed using JPEG2000 Proposed methods are tested with AVIRIS data, promising compression vs. Peak-Signal-to-Noise Ratios (PSNR) are obtained.