Counting the Number of Active Spermatozoa Movements Using Improvement Adaptive Background Learning Algorithm

The most important early stage in sperm infertility research is the detection of sperm objects. The success rate in separating sperm objects from semen fluid has an important role for further analysis. This research performed the detection and calculation of human spermatozoa. The detected sperm was the moving sperm in the video data. An improvement of Adaptive Background Learning was applied to detect the moving sperm. The purpose of this method is to improve the performance of Adaptive Background Learning algorithm in background subtraction process to detect and calculate moving sperm on the microscopic video of sperm fluid. This paper also compared several other background subtraction algorithms to conclude the appropriate background subtraction algorithm for sperm detection and sperm counting. The process done in this research was preprocessing using the Gaussian filter. The next was background subtraction process, followed by morphology operation. To test or validate the detection results of any background subtraction algorithm used, the foreground mask results from the morphological operation were compared to the ground truth of moving sperm image. For visualization purposes, every BLOB area (white object in binary image) on the foreground were given a bounding box to the original frame and the number of BLOB objects present in the foreground mask were counted. This shows that the system had been able to detect and calculate moving sperm. Based on the test results, Adaptive Background Learning method had a value of F-measure of 0.9205 and succeeded in extracting sperm shape close to the original form compared to other methods

Mixture gaussian V2 based microscopic movement detection of human spermatozoa

Healthy and superior sperm is the main requirement for a woman to get pregnant. To find out how the quality of sperm is needed several checks. One of them is a sperm analysis test to see the movement of sperm objects, the analysis is observed using a microscope and calculated manually. The first step in analyzing the scheme is detecting and separating sperm objects. This research is detecting and calculating sperm movements in video data. To detect moving sperm, the background processing of sperm video data is essential for the success of the next process. This research aims to apply and compare some background subtraction algorithms to detect and count moving sperm in microscopic videos of sperm fluid, so we get a background subtraction algorithm that is suitable for the case of sperm detection and sperm count. The research methodology begins with the acquisition of sperm video data. Then, preprocessing using a Gaussian filter, background subtraction, morphological operations that produce foreground masks, and compared with moving sperm ground truth images for validation of the detection results of each background subtraction algorithm. It also shows that the system has been able to detect and count moving sperm. The test results show that the MoG (Mixture of Gaussian) V2 (2 Dimension Variable) algorithm has an f-measure value of 0.9449 and has succeeded in extracting sperm shape close to its original form and is superior compared to other methods. To conclude, the sperm analysis process can be done automatically and efficiently in terms of time

Modified Background Subtraction Statistic Models for Improvement Detection and Counting of Active Spermatozoa Motility

An important early stage in the research of sperm analysis is the phase of sperm detection or separating sperm objects from images/video obtained from observations on semen. The success rate in separating sperm objects from semen fluids has an important role for further analysis of sperm objects. Algorithm or Background subtraction method is a process that can be used to separate moving objects (foreground) and background on sperm video data that tend to uni-modal. In this research, some of the subproject model statistics of substrata model are Gaussian single, Gaussian Mixture Model (GMM), Kernel Density Estimation and compared with some basic subtraction model background algorithm in detecting and counting the number of active spermatozoa. From the results of the tests, the Grimson GMM method has an f-measure value of 0.8265 and succeeded in extracting the sperm form near its original form compared to other method

Modelo heurístico para la determinación de la motilidad en células espermáticas mediante el análisis automático de tracking en video

Hoy en día, con el avance progresivo de la tecnología y la introducción de nuevas tecnicas computacionales ha cambiado la forma de trabajar de los medicos. Este es el caso de los andrólogos quienes tienen a su cargo la importante tarea de ayudar a las parejas a tartar problemas en sus sistemas reproductores con la finalidad de permitirles concebir un hijo, para lo que se require en la mayoria de casos un análisis de fertilidad. Actualmente la forma más usada para realizar este análisis es el método de inspección directa el cual es un procedimiento inexacto, subjetivo, no repetible y difícil de enseñar. El análisis de la motilidad espermática es una parte importante en el análisis de fertilidad y al mismo tiempo es un buen ejemplo del problema de seguimiento a múltiples objetos y video vigilancia desde el punto de vista computacional. El presente proyecto de fin de carrera presenta una solución ante la necesidad de realizar un seguimiento a cada una de las células espermáticas, llamado tracking, la solución planteada pone en práctica técnicas de visión computacional y además propone un modelo heurístico basado en dirección de movimiento y distancia euclidiana para realizar el seguimiento de espermatozoides en videos obtenidos a partir del simulador de células espermáticas también desarrollado en el presente proyecto. El proyecto inicia con el desarrollo de un simulador de células espermáticas, para luego realizar la obtención de muestras de dicho simulador, seguidamente se desarrolló y aplicó un algoritmo para la detección de células espermáticas que fueron usadas como datos de entrada para el algoritmo de Optical Flow así como para la heurística propuesta en el presente trabajo, por último se realizó un estudio estadístico donde se concluye que la heurística propuesta por este proyecto es más eficaz que el algoritmo de Optical Flow.Tesi

Deteksi Dan Perhitungan Spermatozoa Manusia Menggunakan Single Gaussian Background Subtraction

Penelitian tentang penentuan tingkat infertilitas sperma terus dikembangkan. Tahap awal yang penting pada penelitian infertilitas sperma adalah pendeteksian objek sperma. Tingkat keberhasilan dalam memisahkan objek sperma dari cairan semen memiliki peran penting untuk proses analisa selanjutnya. Penelitian ini melalukan deteksi dan perhitungan spermatozoa manusia. Sperma yang terdeteksi adalah sperma yang bergerak pada data video. Untuk melakukan deteksi pada sperma yang bergerak, metode single gaussian background subtraction digunakan. Metode ini sesuai dalam kasus deteksi sperma karena data sperma yang digunakan cenderung uni-modal. Penelitian ini juga membandingkan metode background subtraction lainnya dalam melakukan deteksi sperma. Dari hasil pengujian yang dilakukan, metode single gaussian memiliki nilai f-measure sebesar 0.853 dan berhasil dalam mengekstraksi bentuk sperma mendekati bentuk aslinya dibandingkan dengan metode lainnya =============================================================================================Research about determining infertility rate of sperm is still being under constant development. First important phase on the sperm infertility observation is to detection of sperm object. Success rate of separate sperm with semen fluids has important role for further analytical measure. This research is on its ways to detect and count human's spermatozoa. Detected sperms are moving sperm that is moving on the video. To detect moving sperm, Single Gaussian background subtraction is used. This method fits for sperm detection because the sperm data used are tends to be in unimodal. This research also uses other methods of background subtraction as comparison. The examination result shows, Single Gaussian method has fmeasure value, 0.853 and successfully extracts the sperm shape fully better than other method

Penentuan Abnormalitas Lintasan Pergerakan Spermatozoa Pada Video Mikroskopis Menggunakan Modifikasi Frame Difference Dan Regresi Linear

Penelitian ini mengusulkan beberapa metode dalam deteksi, menghitung jumlah dan pelacakan lintasan pergerakan spermatozoa berbasis video secara cerdas. Tiga bagian besar yang diteliti, yaitu: penentuan abnormalitas morfologi spermatozoa, abnormalitas motility spermatozoa yang terdiri dari modifikasi background subtraction untuk penjejakan spermatozoa dan penentuan abnormalitas pergerakan spermatozoa berdasarkan lintasan. Pada bagian penentuan abnormalitas morfologi digunakan metode SVM (Support Vector Machine) yang dibandingkan dengan metode K-NN (K-Nearest Neighbour) untuk identifikasi abnormalitas pada bentuk kepala spermtozoa. Pada bagian pelacakan kepala spermatozoa digunakan metode M-Frame Difference. Pada bagian ekstraksi fitur untuk penentuan abnormalitas bentuk kepala spermatozoa antara lain area, eccentricity dan ECD sesudah dilakukan BLOB Analysis. Pada bagian kedua dengan memodifikasi beberapa algoritma background subtraction untuk memisahkan objek sperma dari cairan semen. Penelitian ini melalukan deteksi dan perhitungan spermatozoa yang bergerak pada data video. Untuk melakukan deteksi pada sperma yang bergerak, metode Mixture of Gaussian V2 background subtraction digunakan. Metode ini sesuai dalam kasus deteksi sperma karena data sperma yang digunakan cenderung uni-modal. Penelitian ini juga membandingkan metode background subtraction lainnya dalam melakukan deteksi sperma. Bagian ketiga dilakukan penentuan abnormalitas pergerakan berbasis algoritma regresi linaer pada spermatozoa dalam semen, dari lintasan yang terbentuk dianalisa normal tidaknya pergerakan sperma dalam semen. Dari hasil percobaan yang dilakukan video data spermatozoa manusia, ternyata metode di atas didapat posisi pergerakan spermatozoa hasil penjejakan dikenali bentuk lintasannya berdasarkan rata-rata jarak posisinya terhadap garis regresi linier, dengan threshold RMS sebesar 10 terdapat 10 spermatozoa progresif dan 4 spermatozoa non progresif =============================================================================================== sections were examined, namely: determination of morphological abnormalities of spermatozoa, abnormalities of spermatozoa motility which consisted of the modification of background subtraction for tracking spermatozoa, and determination of abnormalities of spermatozoa movement based on the trajectory. In the determination of morphological abnormalities, SVM (Support Vector Machine) method is used which is then compared to the K-NN (K-Nearest Neighbor) method to identify abnormalities in the spermatozoa's head shape. In the tracking section of the spermatozoa head, the M-Frame Difference method was used. Some extraction features performed to determine spermatozoa head shape abnormalities include area, eccentricity, and ECD after BLOB Analysis. The second part modified some background subtraction algorithms to separate sperm objects from semen. This study detected and calculated moving spermatozoa in video data. To detect the moving sperm, the Mixture of Gaussian V2 background subtraction method is used. This method is suitable in the case of sperm detection because sperm data tends to be uni-modal. This study also compared other background subtraction methods for sperm detection. The third part determined the movement abnormalities based on the linear regression algorithm on spermatozoa in cement; the trajectory formed is analyzed whether the movement of sperm in cement is normal or not. From the results of experiments conducted on human spermatozoa video data, it turns out that the above method obtained the position of spermatozoa tracking results identified by the shape of the track based on the average distance of the linear regression line. With an RMS threshold of 10, there are 10 progressive spermatozoa and 4 nonprogressive spermatozoa

An imaging system for microbial corrosion analysis

This paper describes a viable and self-contained imaging system able to assess and quantify the effects of microbial corrosion on metals surface. The proposed image processing uses Scanning Electron Microscope micrographs to analyze bacteria attachment on sample surface and to estimate the degree of degradation of the material. After a preliminary brightness and contrast normalization, which refines the image taken by the operator, the software is able to identify dark spots on the clear metal surface. These are then attributed to singly attached bacteria or to larger clusters, which are the most dangerous ones, as they could overlay corrosion pits. After that, the degradation of the material is evaluated through the quantification of microbial attachment on the surface and through dimensional distribution of bacteria clusters

Measurement techniques for microbial corrosion assessment

L'abstract è presente nell'allegato / the abstract is in the attachmen

Computational phase imaging for biomedical applications

When a sample is illuminated by an imaging field, its fingerprints are left on the amplitude and the phase of the emerging wave. Capturing the information of the wavefront grants us a deeper understanding of the optical properties of the sample, and of the light-matter interaction. While the amplitude information has been intensively studied, the use of the phase information has been less common. Because all detectors are sensitive to intensity, not phase, wavefront measurements are significantly more challenging. Deploying optical interferometry to measure phase through phase-intensity conversion, quantitative phase imaging (QPI) has recently gained tremendous success in material and life sciences. The first topic of this dissertation describes our effort to develop a new QPI setup, named transmission Spatial Light Interference Microscopy (tSLIM), that uses the twisted nematic liquid-crystal (TNLC) modulators. Compared to the established SLIM technique, tSLIM is much less expensive to build than its predecessor (SLIM) while maintaining significant performance. The tSLIM system uses parallel aligned liquid-crystal (PANLC) modulators, has a slightly smaller signal-to-noise Ratio (SNR), and a more complicated model for the image formation. However, such complexity is well addressed by computing. Most importantly, tSLIM uses TNLC modulators that are popular in display LCDs. Therefore, the total cost of the system is significantly reduced. Alongside developing new imaging modalities, we also improved current QPI imaging systems. In practice, an incident field to the sample is rarely perfectly spatially coherent, i.e., plane wave. It is generally partially coherent; i.e., it comprises of many incoherent plane waves coming from multiple directions. This illumination yields artifacts in the phase measurement results, e.g., halo and phase-underestimation. One solution is using a very bright source, e.g., a laser, which can be spatially filtered very well. However, the laser comes at the expense of speckles, which degrades image quality. Therefore, solutions purely based on physical modeling and computations to remove these artifacts, using white-light illumination, are highly desirable. Here, using physical optics, we develop a theoretical model that accurately explains the effects of partial coherence on image information and phase information. The model is further combined with numerical processing to suppress the artifacts, and recover the correct phase information. The third topic is devoted to applying QPI to clinical applications. Traditionally, stained tissues are used in prostate cancer diagnosis instead. The reason is that tissue samples used in diagnosis are nearly transparent under bright field inspection if unstained. Contrast-enhanced microscopy techniques, e.g., phase contrast microscopy (PC) and differential interference contrast microscopy (DIC), can render visibility of the untagged samples with high throughput. However, since these methods are intensity-based, the contrast of acquired images varies significantly from one imaging facility to another, preventing them from being used in diagnosis. Inheriting the merits of PC, SLIM produces phase maps, which measure the refractive index of label-free samples. However, the maps measured by SLIM are not affected by variation in imaging conditions, e.g., illumination, magnification, etc., allowing consistent imaging results when using SLIM across different clinical institutions. Here, we combine SLIM images with machine learning for automatic diagnosis results for prostate cancer. We focus on two diagnosis problems of automatic Gleason grading and cancer vs. non-cancer diagnosis. Finally, we introduce a new imaging modality, named Gradient Light Interference Microscopy (GLIM), which is able to image through optically thick samples using low spatial coherence illumination. The key benefit of GLIM comes from a large numerical aperture of the condenser, which is 0.55 NA, about five times higher than that in SLIM. GLIM has an excellent depth sectioning when recording three-dimensional information of the susceptibility of the sample. We also introduce a model for the image formation of GLIM with an implication that a simple filtering step in the transverse dimension can dramatically improve the sectioning in the axial dimension. With GLIM, one can measure accurately the surface area, volume, and dry mass of a variety of biological samples, ranging from cells that are about tens of microns thick to bovine embryos that are hundreds of microns thick

New fish product ideas generated by European consumers

Food lifestyles are changing; people have less time to spend on food purchase and preparation, therefore leading to increasing demand for new food products. However, around 76% of new food products launched in the market fail within the first year (Nielsen, 2014). One of the most effective ways to enhance new products’ success in the market is by incorporating consumers’ opinions and needs during the New Product Development (NPD) process (Moon et al., 2018). This study aimed to explore the usefulness of a qualitative technique, focus groups, to generate new aquaculture fish product ideas as well as to identify the most relevant product dimensions affecting consumers’ potential acceptance.Peer ReviewedPostprint (published version