A new method for grey-level picture thresholding using the entropy of the histogram

Pun, Thierry

doi:10.1016/0165-1684(80)90020-1

Cited by 576 publications

(214 citation statements)

References 3 publications

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“…Interaction among the pixels is also a reasonable feature tried in two-dimensional Otsu method [8]. In entropy based algorithms proposed by Kapur et al [10] extend the previous work of pun [9] that first uses the concept of entropy for thresholding. This method concludes that when the sum of the background and object entropies reaches its maximum, the threshold value is obtained.…”

Section: IImentioning

confidence: 95%

Segmentation by Thresholding on Medical Imaging – A Survey

Malarvizhi¹

2017

IJRASET

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Medical imaging is one of the important topics in research field. It is the visualization of body parts, tissues, or organs, for use in clinical diagnosis, treatment and disease monitoring. Imaging techniques encompass the fields of radiology, nuclear medicine and optical imaging and image-guided intervention. MRI gives the conservative treatment that does not require incision into the body or the removal of tissue of imaging the cervical spine Magnetic Resonance Imaging (MRI) is a medical imaging technology that uses radio waves and a magnetic field to create detailed images of organs and tissues. The impact of the medical image in various fields are constantly growing. Magnetic Resonance Imaging, is an advanced, state-of-the-art radiology method that produces very clear images of the human body without the use of X-rays. MRI has proven to be highly effective in diagnosing a number of conditions by showing the difference between normal and diseased soft tissues of the body. MRI a powerful diagnostic tool in the assessment of both anatomical deformity and tissue pathology. MRI has tremendous potential in the diagnosis of both intraspinal and osteo-articular disease of the cervical spine. MRI gives extremely clear, detailed images of soft-tissue structures that other imaging techniques cannot achieve. MRI has much greater soft tissue contrast than Computed Tomography, making it useful in neurological, musculoskeletal, cardiovascular, and gynecological imaging. This paper presents the survey of various thresholding techniques used in medical imaging.

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Section: IImentioning

confidence: 95%

Segmentation by Thresholding on Medical Imaging – A Survey

Malarvizhi¹

2017

IJRASET

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“…Probability function p(k, m) is calculated with pixel correlation in neighborhood at each gray level intensity is a major component, are the associated probabilities of object and background used in equations (6) and (7) Where, But, we observe that equation (10) is failing due to the floating precision and there found to be a Δp error, which is equally distributed across the p(k,m) to improve threshold selection criterion.…”

Section: Missing Probability Redistributionmentioning

confidence: 99%

“…Otsu derived threshold based on several statistical measures on image like mean, variance and standard deviation. Shannon introduced information theory based on concept of entropy [5], Pun [6] first applied this concept of entropy to thresholding, Kapur et.al. proposed a method by improving the work of Pun [7].…”

Section: Introductionmentioning

confidence: 99%

Improved Entropic Threshold based on GLSC Histogram with Varying Similarity Measure

Prasad¹,

Divakar²,

Reddy³

2011

IJCA

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“…It should be noted that (8) and (9) are determined by the threshold t, thus they are a function of t. By summing up the local entropies of the object and the background, the second-order local entropy can be obtained by (2) H,o~al(t) = H~2)(t) + H~'(t).…”

Section: 'Iiamentioning

confidence: 99%