Accuracy Improvement of Melanosome Tracking by Error Correction

Okabe, Toshiaki

doi:10.1109/dicta.2013.6691477

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…The accuracy of our conventional method was fair, but it could not recover from tracking failure. Namely, when the method made a mistake at time t, the method continued to track the different melanosome after the time t. To overcome the problem, we propose an error correction process [16]. When the scale and motion of the target are changed suddenly, we suspect that tracking failure is occurred.…”

Section: Introductionmentioning

confidence: 99%

Melanosome Tracking Using Automatic Error Correction

Okabe

2014

IEICE Trans. Inf. & Syst.

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This paper proposes an automatic error correction method for melanosome tracking. Melanosomes in intracellular images are currently tracked manually when investigating diseases, and an automatic tracking method is desirable. We detect all melanosome candidates by SIFT with 2 different parameters. Of course, the SIFT also detects non-melanosomes. Therefore, we use the 4-valued difference image (4-VDimage) to eliminate non-melanosome candidates. After tracking melanosome, we re-track the melanosome with low confidence again from t + 1 to t. If the results from t to t + 1 and from t + 1 to t are different, we judge that initial tracking result is a failure, the melanosome is eliminated as a candidate and re-tracking is carried out. Experiments demonstrate that our method can correct the error and improves the accuracy.

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Section: Introductionmentioning

confidence: 99%

Melanosome Tracking Using Automatic Error Correction

Okabe

2014

IEICE Trans. Inf. & Syst.

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“…These papers propose particle tracking and detection. For particle tracking, researchers have applied dynamic programming approaches , particle filter‐based approach , and the Bayesian approaches . Particle detection and counting has been achieved by classifiers and regression‐based algorithms .…”

Section: Introductionmentioning

confidence: 99%

Particle detection in intracellular images and radius estimation by circle fitting

Kumagai

2014

IEEJ Transactions Elec Engng

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Detecting particles in intracellular images and estimating their radii is important for investigating the causes of clinical conditions. Presently, particles are manually counted by observers, which is time consuming and subjective. Thus, we propose an automatic particle counting and radius estimation method based on pattern recognition techniques. Particles are analyzed by a computer in two processes. During the first process, particles are detected using face recognition via a support vector machine. In the second process, the edge points of a detected particle are distinguished by the intensity difference between the particle and the background. The edge points are extracted and fitted by a circle by a voting technique. The proposed method accurately located 96.04% of the particles detected by manual counting. ImageJ, the particle detection software generally used in cell biology, detected 72.32% of the particles. Therefore, our method yields higher accuracy than ImageJ. Moreover, the effectiveness of radius estimation was also experimentally verified.

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Accuracy Improvement of Melanosome Tracking by Error Correction

Cited by 2 publications

References 16 publications

Melanosome Tracking Using Automatic Error Correction

Melanosome Tracking Using Automatic Error Correction

Particle detection in intracellular images and radius estimation by circle fitting

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