Microscopic medical image classification framework via deep learning and shearlet transform.

Cancer is the second leading cause of death in US after cardiovascular disease. Image-based computer-aided diagnosis can assist physicians to efficiently diagnose cancers in early stages. Existing computer-aided algorithms use hand-crafted features such as wavelet coefficients, co-occurrence matrix features, and recently, histogram of shearlet coefficients for classification of cancerous tissues and cells in images. These hand-crafted features often lack generalizability since every cancerous tissue and cell has a specific texture, structure, and shape. An alternative approach is to use convolutional neural networks (CNNs) to learn the most appropriate feature abstractions directly from the data and handle the limitations of hand-crafted features. A framework for breast cancer detection and prostate Gleason grading using CNN trained on images along with the magnitude and phase of shearlet coefficients is presented. Particularly, we apply shearlet transform on images and extract the magnitude and phase of shearlet coefficients. Then we feed shearlet features along with the original images to our CNN consisting of multiple layers of convolution, max pooling, and fully connected layers. Our experiments show that using the magnitude and phase of shearlet coefficients as extra information to the network can improve the accuracy of detection and generalize better compared to the state-of-the-art methods that rely on hand-crafted features. This study expands the application of deep neural networks into the field of medical image analysis, which is a difficult domain considering the limited medical data available for such analysis.

Automatic measurement of physical mobility in Get-Up-and-Go Test using Kinect sensor.

Get-Up-and-Go Test is commonly used for assessing the physical mobility of the elderly by physicians. This paper presents a method for automatic analysis and classification of human gait in the Get-Up-and-Go Test using a Microsoft Kinect sensor. Two types of features are automatically extracted from the human skeleton data provided by the Kinect sensor. The first type of feature is related to the human gait (e.g., number of steps, step duration, and turning duration); whereas the other one describes the anatomical configuration (e.g., knee angles, leg angle, and distance between elbows). These features characterize the degree of human physical mobility. State-of-the-art machine learning algorithms (i.e. Bag of Words and Support Vector Machines) are used to classify the severity of gaits in 12 subjects with ages ranging between 65 and 90 enrolled in a pilot study. Our experimental results show that these features can discriminate between patients who have a high risk for falling and patients with a lower fall risk.

A user-friendly software to easily count Anopheles egg batches.

BACKGROUND: Studies on malaria vector ecology and development/evaluation of vector control strategies often require measures of mosquito life history traits. Assessing the fecundity of malaria vectors can be carried out by counting eggs laid by Anopheles females. However, manually counting the eggs is time consuming, tedious, and error prone. METHODS: In this paper we present a newly developed software for high precision automatic egg counting. The software written in the Java programming language proposes a user-friendly interface and a complete online manual. It allows the inspection of results by the operator and includes proper tools for manual corrections. The user can in fact correct any details on the acquired results by a mouse click. Time saving is significant and errors due to loss of concentration are avoided. RESULTS: The software was tested over 16 randomly chosen images from 2 different experiments. The results show that the proposed automatic method produces results that are close to the ground truth. CONCLUSIONS: The proposed approaches demonstrated a very high level of robustness. The adoption of the proposed software package will save many hours of labor to the bench scientist. The software needs no particular configuration and is freely available for download on: http://w3.ualg.pt/∼hshah/eggcounter/.