A New Strategy for the Morphological and Colorimetric Recognition of Erythrocytes for the Diagnosis of Forms of Anemia based on Microscopic Color Images of Blood Smears

The detection of red blood cells based on morphology and colorimetric appearance is very important in improving hematology diagnostics. There are automatons capable of detecting certain forms, but these have limitations with regard to the formal identification of red blood cells because they consider certain cells to be red blood cells when they are not and vice versa. Other automata have limitations in their operation because they do not cover a sufficient area of the blood smear. In spite of their performance, biologists have very often resorted to the manual analysis of blood smears under an optical microscope for a morphological and colorimetric study. In this paper, we present a new strategy for semi-automatic identification of red blood cells based on their isolation, their automatic color segmentation using Otsu's algorithm and their morphology. The algorithms of our method have been implemented in the programming environment of the scientific software MATLAB resulting in an artificial intelligence application. The application, once launched, allows the biologist to select a region of interest containing the erythrocyte to be characterized, then a set of attributes are computed extracted from this target red blood cell. These attributes include compactness, perimeter, area, morphology, white and red proportions of the erythrocyte, etc. The types of anemia treated in this work concern the iron-deficiency, sickle-cell or falciform, thalassemia, hemolytic, etc. forms. The results obtained are excellent because they highlight different forms of anemia contracted in a patient.Comment: ISIS

Microcalcifications Detection Using Image And Signal Processing Techniques For Early Detection Of Breast Cancer

Breast cancer has transformed into a severe health problem around the world. Early diagnosis is an important factor to survive this disease. The earliest detection signs of potential breast cancer that is distinguishable by current screening techniques are the presence of microcalcifications (MCs). MCs are small crystals of calcium apatite and their normal size ranges from 0.1mm to 0.5mm single crystals to groups up to a few centimeters in diameter. They are the first indication of breast cancer in more than 40% of all breast cancer cases, making their diagnosis critical. This dissertation proposes several segmentation techniques for detecting and isolating point microcalcifications: Otsu’s Method, Balanced Histogram Thresholding, Iterative Method, Maximum Entropy, Moment Preserving, and Genetic Algorithm. These methods were applied to medical images to detect microcalcifications. In this dissertation, results from the application of these techniques are presented and their efficiency for early detection of breast cancer is explained. This dissertation also explains theories and algorithms related to these techniques that can be used for breast cancer detection

Automatic Segmentation of Trees in Dynamic Outdoor Environments

Segmentation in dynamic outdoor environments can be difficult when the illumination levels and other aspects of the scene cannot be controlled. Specifically in orchard and vineyard automation contexts, a background material is often used to shield a camera\u27s field of view from other rows of crops. In this paper, we describe a method that uses superpixels to determine low texture regions of the image that correspond to the background material, and then show how this information can be integrated with the color distribution of the image to compute optimal segmentation parameters to segment objects of interest. Quantitative and qualitative experiments demonstrate the suitability of this approach for dynamic outdoor environments, specifically for tree reconstruction and apple flower detection application

Segmentation of pathology microscopic images

The light microscopic analysis of the number and shape of cells in pathology is important for the diagnosis and assessment of clinical behavior of disease conditions. The fundamental step of this work is to separate the cells from the background. To segment objects from such uneven background images, a fixed threshold is not suitable. The authors have proposed a new adaptive thresholding method using variational theory. In this paper, this method is introduced to segment pathological images under the light microscope. The comparison of the segmentation results of the authors' method and Otsu's (1979) thresholding method shows the advantage of the authors' method.published_or_final_versio

Parallel Processing of Image Segmentation Data Using Hadoop

The use of sequential programming is slowly getting replaced by distributed and parallel computing which is widely being used in computing industries to handle tasks with big data and various high-end computing applications comprising of huge image and video data banks. Moreover, image processing using parallel computation is also gaining momentum in today's technological era. Nowadays researchers are coming up with various methodologies to tackle high scale image processing applications by implementing parallel computing methodologies to carry out the specified image processing application task and simultaneously checking its performance against sequential programming. At the same time there are constraints on what can be done to maximize the task performance using high end multi-core CPU's with advanced buses and interconnects that offer high bandwidth with low system latency. It is to be noted that there is no availability of standardized image processing task which can be used to evaluate a single node system. In this paper, we propose an efficient parallel processing algorithm to perform the task of image segmentation with the foremost aim to analyze the threshold of data size at which the proposed method outperforms sequential programming method in terms of task execution time by analyzing the distribution of average CPU cores usage and its threads over the execution time. The proposed methodology could be useful for researchers, as it can perform multiple image segmentation in parallel, which can save a lot of time of the user. For the purpose of comparison, we also implemented the same image segmentation task using sequential method of programming in an integrated development environment platform

Analysis of objects in binary images

Digital image processing techniques are typically used to produce improved digital images through the application of successive enhancement techniques to a given image or to generate quantitative data about the objects within that image. In support of and to assist researchers in a wide range of disciplines, e.g., interferometry, heavy rain effects on aerodynamics, and structure recognition research, it is often desirable to count objects in an image and compute their geometric properties. Therefore, an image analysis application package, focusing on a subset of image analysis techniques used for object recognition in binary images, was developed. This report describes the techniques and algorithms utilized in three main phases of the application and are categorized as: image segmentation, object recognition, and quantitative analysis. Appendices provide supplemental formulas for the algorithms employed as well as examples and results from the various image segmentation techniques and the object recognition algorithm implemented