Cellular tracking in time-lapse phase contrast images

The quantitative analysis of live cells is a key issue in evaluating biological processes. The current clinical practice involves the application of a tedious and time consuming manual tracking procedure on large amount of data. As a result, automatic tracking systems are currently developed and evaluated. However, problems caused by cellular division, agglomeration, Brownian motion and topology changes are difficult issues that have to be accommodated by automatic tracking techniques. In this paper, we detail the development of a fully automated multi-target tracking system that is able to deal with Brownian motion and cellular division. During the tracking process our approach includes the neighbourhood relationship and motion history to enforce the cellular tracking continuity in the spatial and temporal domain. The experimental results reported in this paper indicate that our method is able to accurately track cellular structures in time-lapse data

Improved microbubble (MB) Localisation Using Particle Detecting algorithm:Evaluation of Algorithm Performance for Different Beamforming Methods

International audienceThe performance of image analysis techniques (particle detection) on contrast enhanced ultrasound (CEUS) images could be enhanced by using it in combination with the right beamformer (BF). The current study investigates the best performing combination of a particle detecting algorithm (Kanoulas et al. 2019) with four beamformers (BFs), classical and adaptive. In a series of in silico experiments, adjacent MBs are placed in distances comparable to the lateral resolution limit, the CEUS images of the MBs were simulated in FieldII, and finally beamformed with the four methods. The images were processed with the MB detection algorithm and the results were evaluated by the true detections (TD), missed MBs, spurious detections, and localisation uncertainty (LU). For the smallest distances all methods deteriorate but the MV methods provided 4-12% more TD. For the intermediate distances the TD were comparable for all BFs but the adaptive methods provided lower LU. When a set of evaluation metrics is used, the adaptive methods provide marginally but systematically improved results which suggests that, under the appropriate imaging conditions, they could be used to enhance vessel mapping

3D Gray Level Co-Occurrence Matrix Based Classification of Favor Benign and Borderline Types in Follicular Neoplasm Images

Abstract Since the efficiency of treatment of thyroid disorder depends on the risk of malignancy, indeterminate follicular neoplasm (FN) images should be classified. The diagnosis process has been done by visual interpretation of experienced pathologists. However, it is difficult to separate the favor benign from borderline types. Thus, this paper presents a classification approach based on 3D nuclei model to classify favor benign and borderline types of follicular thyroid adenoma (FTA) in cytological specimens. The proposed method utilized 3D gray level co-occurrence matrix (GLCM) and random forest classifier. It was applied to 22 data sets of FN images. Furthermore, the use of 3D GLCM was compared with 2D GLCM to evaluate the classification results. From experimental results, the proposed system achieved 95.45% of the classification. The use of 3D GLCM was better than 2D GLCM according to the accuracy of classification. Consequently, the proposed method probably helps a pathologist as a prescreening tool

Identifikasi Sel Darah Merah Bertumpuk Menggunakan Pohon Keputusan Fuzzy Berbasis Gini Index

Pendekatan teknik data mining diusulkan untuk identifikasi sel darahmerah bertumpuk pada citra makroskopik sel darah untuk meningkatkan akurasipenghitungan jumlah sel darah merah. Fitur yang digunakan adalah geometri danwarna. Fitur geometri terdiri dari luasan dan eksentrisitas sel. Pada prosesidentifikasi digunakan pendekatan fuzzy. Setiap fitur direpresentasikan denganfungsi keanggotaan fuzzy. Identifikasi dilakukan berdasarkan aturan yangdiperoleh dari pohon keputusan fuzzy yang dibangkitkan. Pencabangan multisplitdigunakan pada pohon keputusan fuzzy. Pengukuran split atribut menggunakannilai gini index. Hasil pengujian pada 10 citra makroskopik sel darah yangmengandung 532 sel darah merah menunjukkan bahwa metode yang diusulkanmemiliki rata-rata akurasi sebesar 96,14%. Dengan akurasi yang tinggidiharapkan dapat meningkatkan akurasi diagnosis penyakit berdasarkan jumlahsel darah merah

Overlapping Cervical Nuclei Separation using Watershed Transformation and Elliptical Approach in Pap Smear Images

In this study, a robust method is proposed for accurately separating overlapping cell nuclei in cervical microscopic images. This method is based on watershed transformation and an elliptical approach. Since the watershed transformation process of taking the initial seed is done manually, the method was developed to obtain the initial seed automatically. Total initial seeds at this stage represents the number of nuclei that exist in the image of a pap smear, either overlapping or not. Furthermore, a method was developed based on an elliptical approach to obtain the area of each of the nuclei automatically. This method can successfully separate several (more than two) clustered cell nuclei. In addition, the proposed method was evaluated by experts and was proven to have better results than methods from previous studies in terms of accuracy and execution time. The proposed method can determine overlapping and non-overlapping boundaries of nuclei fast and accurately. The proposed method provides better decision-making on areas with overlapping nuclei and can help to improve the accuracy of image analysis and avoid information loss during the process of image segmentation

Vision Analysis of Pack Ice for Potential Use in a Hazard Warning and Avoidance System

Ships travelling through pack ice are exposed to collisions that can result in structural damage to the hull. The GEM project at Memorial University has developed ice-ship interaction simulation software that allows study of the impact forces applied on a ship when it maneuvers through pack ice [1]. Such capability is useful in order to predict the collisions that would potentially affect the structural integrity and operational performance of ships and floating offshore structures. GEM is capable of simulating transit through complex pack ice formations at a rate much faster than real time. If hyper-real time simulation were available in a real operational setting, with actual ice, it would permit a variety of benefits, including general operational planning. If the near field ice information were sufficiently accurate, GEM could also be used in a “feed forward” near-field hazard warning and avoidance system (HWAS)