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We propose a fully automatic method for segmenting myelin and axon from microscopy images of excised mouse spinal cord based on Convolutional Neural Networks (CNNs) and Deep Convolutional Encoder-Decoder. We compare a two-class CNN, multi-class CNN, and multi-class deep convolutional encoder-decoder with traditional methods. The CNN method gives a pixel-wise accuracy of 79.7% whereas an Active Contour...
Microneedle insertion is useful for elucidating the processes in living cells and their organelles. However, mechanical puncture by the needle causes traumatic damage to the cell. As a less invasive process, we have developed intracellular needle insertion using femtosecond laser cell ablation. To quickly and precisely position the spot ablated by the laser, we accurately located the needle tip in...
Localization microscopy (LM) allows to acquire pointillistic superresolution images of biological structures on the nanoscale. However, current structure reconstruction and segmentation approaches suffer from either exclusion of small structures or strong dependence on a-priori knowledge. We propose reconstruction methods based on compressed sensing (CS) denoising in combination with the isodata threshold...
In this paper, we consider confocal microscopy based vessel segmentation with optimized features and random forest classification. By utilizing multi-scale vessel-specific features tuned to capture curvilinear structures such as Frobenius norm of the Hessian eigenvalues, Laplacian of Gaussians (LoG), oriented second derivative, line detector and intensity masked with LoG scale map. we obtain better...
Hematological disorders of leukocytes (white blood corpuscles) are very frequent in medical practices. Each year, the number of new cases of leukemia increased with a high mortality rate what can cause a delay in diagnosis and treatment of leukemia. The hematologist faces difficulty in classifying white blood cells included cancerous leukemia cells. Currently, the diagnosis of hematological disorder...
This paper illustrates a comprehensive review about malaria identification method on thin blood smear, including conventional identification by expert and computer-aided identification. Even though effective way to overcome the malaria has been discovered, in fact these cases is still growing among developed country and murdering 1 million people annually; 90% of them are children from Africa. There...
Malaria is a serious health problem in Indonesia caused by malaria parasites. Early detection of Malaria is an important step to an effective treatment. Malaria parasite identification should be carried out based on observation on at least 100 fields view strong magnification of thick blood smears. Malaria parasite detection process is usually carried out with a microscope observation. But it consumes...
Blood cell analysis, including blood cell counting, is the key point for modern pathological study as well as medical diagnosis. Taking into account both resources and environment of the medical research, analyzing blood cells under the microscope, instead of dedicated blood cell analyzer, provides a more intuitive and convenient way for research uses. This paper aims to provide a method to count...
Circulating tumor cells (CTCs) is an informative biomarker which assists pathologists in early diagnosis and evaluating therapeutic effects of patients with malignant tumors. The blood from a cancer patient is analyzed by a microscope and a large number of pictures including many cells are generated for each case. Thus, analyzing them is time-consuming work for pathologists, and misdiagnosis may happen...
Proper recognition of microscopic sperm cells in video images is an important step in diagnosis and treatment of male infertility. The small sizes of the sperm cells make their segmentation and detection an important stage in the microscopic images analysis. Histogram-based thresholding schemes are one of the common approaches for this purpose. This paper proposes a non-linear amplitude compression...
Coccidia is an intestinal parasite that infects animals and causes Coccidiosis disease. Substantial animal mortality can be faced within several days of infection if it is not diagnosed or treated at the early stages of infection. Therefore, an urgent diagnostic tool has become necessary to tackle the spread of disease and to avoid animal death and subsequent business losses. Vets detect the disease...
This paper presents modification of conventional watershed algorithm for cell segmentation in microscopic images of desmoglein-3 stained specimen. Presented method combines color deconvolution for ihc marker separation and GVF for watershed segmentation. Conventional watershed is highly noise sensitive, which often occurs in microscopy images. Suggested solution considerably reduces over segmentation...
Lung cancer is one among the major causes of cancer related deaths. Fortunately, an early stage diagnosis can increase the survival rates of the patients. Sputum cytology is one of the easiest and cost-effective method for lung cancer diagnosis. Chances of misdiagnosis and sampling error related to sputum cytology led to the concept of malignancy associated changes. Malignancy associated changes (MAC)...
Necessity of meat quality evaluation has become important in the present scenario. Meat and meat products are potential vehicles of pathogens that cause hazards to human beings. The methods for freshness and quality assessment of meat can be chemical, sensory and spectroscopy and has several advantages and disadvantages. Need for a non-destrctive method has led to colour and computer image analysis,...
Segmentation is a key component of several bio-medical image processing systems. Recently, segmentation methods based on supervised learning such as deep convolutional networks have enjoyed immense success for natural image datasets and biological datasets alike. These methods require large volumes of data to avoid overfitting which limits their applicability. In this work, we present a transfer learning...
Robust cell detection plays a key role in the development of reliable methods for automated analysis of microscopy images. It is a challenging problem due to low contrast, variable fluorescence, weak boundaries, conjoined and overlapping cells, causing most cell detection methods to fail in difficult situations. One approach for overcoming these challenges is to use cell proposals, which enable the...
The progress in imaging techniques have allowed the study of various aspect of cellular mechanisms. To isolate individual cells in live imaging data, we introduce an elegant image segmentation framework that effectively extracts cell boundaries, even in the presence of poor edge details. Our approach works in two stages. First, we estimate pixel interior/border/exterior class probabilities using random...
A vast amount of toxicological data can be obtained from feature analysis of cells treated in vitro. However, this requires microscopic image segmentation of cells. To this end, we propose a new strategy, namely Supervised Normalized Cut Segmentation (SNCS), to segment cells that partially overlap and have a large amount of curved edges. SNCS approach is a machine learning based method, where loosely...
Quantitative co-localization analysis with fluorescent microscopy is a common approach to assess the spatial co-ordination of molecules and thus to understand their functions in biological processes. However, the co-localization analysis results might not be consistent due to various imaging conditions and different quantification methods used. We propose a novel method to separate a co-localization...
We introduce a simple, yet effective, procedure for accurate classification of connected components embedded in biological images. In our method, a training set is generated from user-delineated features of manually-labeled examples; we subsequently train a classifier using the resultant training set. The overall process is described using imaging data acquired from an India-ink perfused C57BL/6J...
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