Counting clustered cells using distance mapping

Khan, Haider Adnan; Maruf, Golam Morshed

doi:10.1109/iciev.2013.6572677

Cited by 13 publications

(9 citation statements)

References 7 publications

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“…We compare these distributions to an exponential distribution with a value λ = 1, which is on the order of the mean waiting times seen in the experiments. The exponential distribution is expected for a Poisson process and was chosen for comparison because of the extensive body of literature showing its fitness for modeling uncorrelated random processes (Lawler and Limic, 2010). If the waiting times were uncorrelated and truly random, they should follow an exponential distribution; however, the measured waiting times decay much more slowly (Fig.…”

Section: Resultsmentioning

confidence: 99%

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Determining the scales of collective entrainment in collision-driven bed load

Lee

Jerolmack

2018

Earth Surf. Dynam.

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Abstract. Fluvial bed-load transport is notoriously unpredictable, especially near the threshold of motion where stochastic fluctuations in sediment flux are large. Laboratory and field observations suggest that particles are entrained collectively, but this behavior is not well resolved. Collective entrainment introduces new length scales and timescales of correlation into probabilistic formulations of bed-load flux. We perform a series of experiments to directly quantify spatially clustered movement of particles (i.e., collective motion), using a steep-slope 2-D flume in which centimeter-scale marbles are fed at varying rates into a shallow and turbulent water flow. We observe that entrainment results exclusively from particle collisions and is generally collective, while particles deposit independently of each other. The size distribution of collective motion events is roughly exponential and constant across sediment feed rates. The primary effect of changing feed rate is simply to change the entrainment frequency, although the relation between these two diverges from the expected linear form in the slowly driven limit. The total displacement of all particles entrained in a collision event is proportional to the kinetic energy deposited in the bed by the impactor. The first-order picture that emerges is similar to generic avalanching dynamics in sandpiles: “avalanches” (collective entrainment events) of a characteristic size relax with a characteristic timescale regardless of feed rate, but the frequency of avalanches increases in proportion to the feed rate. The transition from intermittent to continuous bed-load transport then results from the progressive merger of entrainment avalanches with increasing transport rate. As most bed-load transport occurs in the intermittent regime, the length scale of collective entrainment should be considered a fundamental addition to any probabilistic bed-load framework.

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Section: Resultsmentioning

confidence: 99%

“…Once images are acquired, approximate particle centers are located using a hybrid form of the algorithms outlined in Khan and Maruf (2013) and Parthasarathy (2012). Using this method it is possible to obtain particle centers that are accurate to better than 1 mm.…”

Section: Data Acquisition and Analysismentioning

confidence: 99%

Determining the scales of collective entrainment in collision-driven bed load

Lee

Jerolmack

2018

Earth Surf. Dynam.

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“…Later, Khan and Maruf () proposed a new framework for segmentation as well as counting by using cell centroids detection scheme in microscopic images. This scheme was developed for identification and for counting cells with circular shape and a high probability of occlusion.…”

Section: Related Workmentioning

confidence: 99%

Modified cuckoo search algorithm in microscopic image segmentation of hippocampus

Chakraborty

Chatterjee

Dey³

et al. 2017

Microscopy Res & Technique

107

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Microscopic image analysis is one of the challenging tasks due to the presence of weak correlation and different segments of interest that may lead to ambiguity. It is also valuable in foremost meadows of technology and medicine. Identification and counting of cells play a vital role in features extraction to diagnose particular diseases precisely. Different segments should be identified accurately in order to identify and to count cells in a microscope image. Consequently, in the current work, a novel method for cell segmentation and identification has been proposed that incorporated marking cells. Thus, a novel method based on cuckoo search after pre-processing step is employed. The method is developed and evaluated on light microscope images of rats' hippocampus which used as a sample for the brain cells. The proposed method can be applied on the color images directly. The proposed approach incorporates the McCulloch's method for lévy flight production in cuckoo search (CS) algorithm. Several objective functions, namely Otsu's method, Kapur entropy and Tsallis entropy are used for segmentation. In the cuckoo search process, the Otsu's between class variance, Kapur's entropy and Tsallis entropy are employed as the objective functions to be optimized. Experimental results are validated by different metrics, namely the peak signal to noise ratio (PSNR), mean square error, feature similarity index and CPU running time for all the test cases. The experimental results established that the Kapur's entropy segmentation method based on the modified CS required the least computational time compared to Otsu's between-class variance segmentation method and the Tsallis entropy segmentation method. Nevertheless, Tsallis entropy method with optimized multi-threshold levels achieved superior performance compared to the other two segmentation methods in terms of the PSNR.

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“…There are several methods used for counting purposes that consider a clumped regions. Some of the methods are distance transform [8], edge detection [9] and Circle Hough Transform (CHT) [5] [6]. Among this method, CHT is mentioned to perform better for estimating number of cell in the clumped regions [10].…”

Section: Introductionmentioning

confidence: 99%

White blood cell counting analysis of blood smear images using various segmentation strategies

Safuan

Tomari

Zakaria

et al. 2017

AIP Conference Proceedings

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Abstract. In white blood cell (WBC) diagnosis, the most crucial measurement parameter is the WBC counting. Such information is widely used to evaluate the effectiveness of cancer therapy and to diagnose several hidden infection within human body. The current practice of manual WBC counting is laborious and a very subjective assessment which leads to the invention of computer aided system (CAS) with rigorous image processing solution. In the CAS counting work, segmentation is the crucial step to ensure the accuracy of the counted cell. The optimal segmentation strategy that can work under various blood smeared image acquisition conditions is remain a great challenge. In this paper, a comparison between different segmentation methods based on color space analysis to get the best counting outcome is elaborated. Initially, color space correction is applied to the original blood smeared image to standardize the image color intensity level. Next, white blood cell segmentation is performed by using combination of several color analysis subtraction which are RGB, CMYK and HSV, and Otsu thresholding. Noises and unwanted regions that present after the segmentation process is eliminated by applying a combination of morphological and Connected Component Labelling (CCL) filter. Eventually, Circle Hough Transform (CHT) method is applied to the segmented image to estimate the number of WBC including the one under the clump region. From the experiment, it is found that G-S yields the best performance.

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Counting clustered cells using distance mapping

Cited by 13 publications

References 7 publications

Determining the scales of collective entrainment in collision-driven bed load

Determining the scales of collective entrainment in collision-driven bed load

Modified cuckoo search algorithm in microscopic image segmentation of hippocampus

White blood cell counting analysis of blood smear images using various segmentation strategies

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