Automated red blood cells extraction from holographic images using fully convolutional neural networks

In this paper, we present two models for automatically extracting red blood cells (RBCs) from RBCs holographic images based on a deep learning fully convolutional neural network (FCN) algorithm. The first model, called FCN-1, only uses the FCN algorithm to carry out RBCs prediction, whereas the second model, called FCN-2, combines the FCN approach with the marker-controlled watershed transform segmentation scheme to achieve RBCs extraction. Both models achieve good segmentation accuracy. In addition, the second model has much better performance in terms of cell separation than traditional segmentation methods. In the proposed methods, the RBCs phase images are first numerically reconstructed from RBCs holograms recorded with off-axis digital holographic microscopy. Then, some RBCs phase images are manually segmented and used as training data to fine-tune the FCN. Finally, each pixel in new input RBCs phase images is predicted into either foreground or background using the trained FCN models. The RBCs prediction result from the first model is the final segmentation result, whereas the result from the second model is used as the internal markers of the marker-controlled transform algorithm for further segmentation. Experimental results show that the given schemes can automatically extract RBCs from RBCs phase images and much better RBCs separation results are obtained when the FCN technique is combined with the marker-controlled watershed segmentation algorithm. © 2017 Optical Society of America.1

Segmentasi dan Klasifikasi Citra Sel Darah Putih Menggunakan Metode Pre-Processing Self-Dual Multiscale Morphological Toggle

Kemajuan teknologi memberikan kemudahan untuk menyelesaikan permasalahan. Dengan adanya pengolahan citra digital, penghitungan dan identifikasi jenis sel darah putih pada blood smear image yang sebelumnya dilakukan secara konvensional kini dapat dilakukan secara otomatis. Meskipun demikian, hingga saat ini belum ada standar metode yang diakui dunia. Tugas akhir ini mengusulkan sebuah metode self-dual multiscale morphological toggle (SMMT) sebagai metode preprocessing dari blood smear image. Nukleus dan sitoplasma sel darah putih dideteksi dan disegmentasi secara terpisah. Segmentasi nukleus dilakukan menggunakan metode level set. Segmentasi sitoplasma dilakukan menggunakan metode-metode morfologi matematika, yaitu bottom hat, flood fill, dan watershed. Kemudian citra hasil segmentasi diekstrak fitur-fiturnya dan diklasifikasi menggunakan metode decision tree. Data akhir yang didapat adalah jumlah sel darah putih dalam blood smear image dan jenis dari masing-masing sel darah putih yang telah tersegmentasi. Uji coba yang dilakukan terhadap 247 ctra training dan 30 citra testing menunjukkan bahwa metode ini dapat memberikan hasil segmentasi hapusan darah dan klasifikasi sel darah putih yang akurat dengan rata-rata akurasi, specificity dan sensitivity sebesar 87,78%, 71,94%, dan 88,64%. ======================================================================================================== Using digital image processing, counting and identification of white blood cell types, which previously performed conventionally, now can be done automatically. However, there is no standard method that is recognized worldwide yet. In this undergraduate thesis, we propose self-dual multiscale morphological toggle (SMMT) as pre-processing method. Nucleus and cytoplasm of white blood cell will be detected and segmented separately. Nucleus segmentation will be performed using level-set method. Cytoplasm segmentation will be done using mathematical morphology methods, namely bottom hat, flood fill, and watershed. Then, segmented image’s features will be extracted and classified using decision tree method. Data produced through these processes will be white blood cell counting and types of each segmented white blood cell. The experiment was performed for 247 training images and 30 testing images. The result shows that these methods give accurate white blood cell classification with the average accuracy, specificity, and sensitivity of 87,78%, 71,94%, and 88,64%

Automated Low-Cost Malaria Detection System in Thin Blood Slide Images Using Mobile Phones

Malaria, a deadly disease which according to the World Health Organisation (WHO) is responsible for the fatal illness in 200 million people around the world in 2010, is diagnosed using peripheral blood examination. The work undertaken in this research programme aims to develop an automated malaria parasite-detection system, using microscopic-image processing, that can be incorporated onto mobile phones. In this research study, the main objective is to achieve the performance equal to or better than the manual microscopy, which is the gold standard in malaria diagnosis, in order to produce a reliable automated diagnostic platform without expert intervention, for the effective treatment and eradication of the deadly disease. The work contributed to the field of mathematical morphology by proposing a novel method called the Annular Ring Ratio transform for blood component identification. It has also proposed an automated White Blood Cell and Red Blood Cell differentiation algorithm, which when combined with ARR transform method, has wide applications not only for malaria diagnosis but also for many blood related analysis involving microscopic examination. The research has undertaken investigations on infected cell identification which aids in the calculation of parasitemia, the measure of infection. In addition, an automated diagnostic tool to detect the sexual stage (gametocytes) of the species P.falciparum for post-treatment malaria diagnosis was developed. Furthermore, a parallel investigation was carried out on automated malaria diagnosis on fluorescent thin blood films and a WBC and infected cell differentiation algorithm was proposed. Finally, a mobile phone application based on the morphological image processing algorithms proposed in this thesis was developed. A complete malaria diagnostic unit using the mobile phones attached to a portable microscope was set up which has enormous potential not only for malaria diagnosis but also for the blood parasitological field where advancement in medical diagnostics using cellular smart phone technology is widely acknowledged

Semiautomatic White Blood Cell Segmentation Based on Multiscale Analysis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)This paper approaches novel methods to segment the nucleus and cytoplasm of white blood cells (WBC). This information is the basis to perform higher level tasks such as automatic differential counting, which plays an important role in the diagnosis of different diseases. We explore the image simplification and contour regularization resulting from the application of the selfdual multiscale morphological toggle (SMMT), an operator with scale-space properties. To segment the nucleus, the image preprocessing with SMMT has shown to be essential to ensure the accuracy of two well-known image segmentations techniques, namely, watershed transform and Level-Set methods. To identify the cytoplasm region, we propose two different schemes, based on granulometric analysis and on morphological transformations. The proposed methods have been successfully applied to a large number of images, showing promising segmentation and classification results for varying cell appearance and image quality, encouraging future works.171250256Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Fundacao Araucaria [17588]Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Fundacao Araucaria [17588