NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation

Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A fully automatic and optimized segmentation process for different datasets is a prerequisite for classifying and diagnosing indirect immunofluorescence (IIF) raw data. This study describes a deterministic computational neuroscience approach for identifying cells and nuclei. It is very different from the conventional neural network approaches but has an equivalent quantitative and qualitative performance, and it is also robust against adversative noise. The method is robust, based on formally correct functions, and does not suffer from having to be tuned on specific data sets. Results: This work demonstrates the robustness of the method against variability of parameters, such as image size, mode, and signal-to-noise ratio. We validated the method on three datasets (Neuroblastoma, NucleusSegData, and ISBI 2009 Dataset) using images annotated by independent medical doctors. Conclusions: The definition of deterministic and formally correct methods, from a functional and structural point of view, guarantees the achievement of optimized and functionally correct results. The excellent performance of our deterministic method (NeuronalAlg) in segmenting cells and nuclei from fluorescence images was measured with quantitative indicators and compared with those achieved by three published ML approaches

FEATURES EXTRACTION OF HEP-2 IMMUNOFLUORESCENCE PATTERNS BASED ON TEXTURE AND REGION OF INTEREST TECHNIQUES

Autoimmune disease is a disease that happens when improper immune response in the body fighting against substance, cells and tissues that naturally exists and needed in human’s body. This will later on cause autoantibody disease such as SLE where internal organ failed to perform their basic functions. Antinuclear antibody (ANA) test is a way to test the presence of autoantibodies in individual blood serum. This study focuses on ANA test that is done using indirect immunofluorescence HEp-2 cell coating slides that are used to place the blood serum. However, there are several problems encountered with current technique, such as inaccuracy of the result as the result is viewed by naked eyes of operator. There is no objective definition for positive, negative or border line of infection. This project involves developing features extraction technique of HEp-2 cell of 2 main patterns namely Nucleolar and Centromere using texture and region of interest technique. Next, to design an algorithm that can automatically identify the 2 patterns of the HEp-2 cell tested using ANA. To execute features extraction, image pre-processing is performed to enhance image in terms of its intensity, brightness and contrast. Only clear and good input image will produce good results. Image segmentation will be done after pre-processing completed to further enhance the image according to its edge or region to be used for the input image. Different methods of features extraction will be used and compared for better outcome. To differentiate between one pattern from another, image classification is done by evaluating the properties of internal image from features extraction and a boundary is drawn between Centromere and Nucleolar pattern. The result shows four different types of properties of internal cells which are homogeneity, contrast, energy and correlation. After analysis has been done, energy between Centromere and Nucleolar are different from each other and used to classify the pattern in SVM classifier. Tools used in this study are MATLAB software and image processing tools in MATLAB

Local and deep texture features for classification of natural and biomedical images

Developing efficient feature descriptors is very important in many computer vision applications including biomedical image analysis. In the past two decades and before the popularity of deep learning approaches in image classification, texture features proved to be very effective to capture the gradient variation in the image. Following the success of the Local Binary Pattern (LBP) descriptor, many variations of this descriptor were introduced to further improve the ability of obtaining good classification results. However, the problem of image classification gets more complicated when the number of images increases as well as the number of classes. In this case, more robust approaches must be used to address this problem. In this thesis, we address the problem of analyzing biomedical images by using a combination of local and deep features. First, we propose a novel descriptor that is based on the motif Peano scan concept called Joint Motif Labels (JML). After that, we combine the features extracted from the JML descriptor with two other descriptors called Rotation Invariant Co-occurrence among Local Binary Patterns (RIC-LBP) and Joint Adaptive Medina Binary Patterns (JAMBP). In addition, we construct another descriptor called Motif Patterns encoded by RIC-LBP and use it in our classification framework. We enrich the performance of our framework by combining these local descriptors with features extracted from a pre-trained deep network called VGG-19. Hence, the 4096 features of the Fully Connected 'fc7' layer are extracted and combined with the proposed local descriptors. Finally, we show that Random Forests (RF) classifier can be used to obtain superior performance in the field of biomedical image analysis. Testing was performed on two standard biomedical datasets and another three standard texture datasets. Results show that our framework can beat state-of-the-art accuracy on the biomedical image analysis and the combination of local features produce promising results on the standard texture datasets.Includes bibliographical reference

A Convolutional Neural Network for the Automatic Diagnosis of Collagen VI related Muscular Dystrophies

The development of machine learning systems for the diagnosis of rare diseases is challenging mainly due the lack of data to study them. Despite this challenge, this paper proposes a system for the Computer Aided Diagnosis (CAD) of low-prevalence, congenital muscular dystrophies from confocal microscopy images. The proposed CAD system relies on a Convolutional Neural Network (CNN) which performs an independent classification for non-overlapping patches tiling the input image, and generates an overall decision summarizing the individual decisions for the patches on the query image. This decision scheme points to the possibly problematic areas in the input images and provides a global quantitative evaluation of the state of the patients, which is fundamental for diagnosis and to monitor the efficiency of therapies.Comment: Submitted for review to Expert Systems With Application