Edge detection in prostatic ultrasound images using integrated edge maps

Aarnink, R.G.; Pathak, Dev S.; Rosette, J.J.M.H.C. de la; Debruyne, F.M.J.; Kim, Y.; Wijkstra, Hessel

doi:10.1016/s0041-624x(97)00126-1

Cited by 55 publications

(31 citation statements)

References 14 publications

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“…These statistics are computed from the histogram of the gray levels of the image and depend only on the individual pixels and not on their neighborhood. In the prostate, first-order statistics have been used in the following models: the intensity profile model [47], gradient models [48,49], models using the gray level threshold of the regions extracted from a neural network [50], a radial basis relief model [51], an instantaneous variation coefficient (ICOV) model [52], a model using the local standard deviation in a multiresolution framework [53], posterior probability models [9,54], mixture probability distribution models [42,43,55,56], or models that are combined in many other ways [13,15,57,58]. Feng et al [49] proposed a weighted combination of gradient and probability distribution functions.…”

Section: Appearancementioning

confidence: 99%

A survey of prostate modeling for image analysis

Chilali¹,

Ouzzane²,

Diaf³

et al. 2014

Computers in Biology and Medicine

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Computer technology is widely used for multimodal image analysis of the prostate gland. Several techniques have been developed, most of which incorporate a priori knowledge extracted from organ features. Knowledge extraction and modeling are multi-step tasks. Here, we review these steps and classify the modeling according to the data analysis methods employed and the features used. We conclude with a survey of some clinical applications where these techniques are employed.

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Section: Appearancementioning

confidence: 99%

A survey of prostate modeling for image analysis

Chilali¹,

Ouzzane²,

Diaf³

et al. 2014

Computers in Biology and Medicine

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“…Many studies have been performed but it has not yet come to clinical application. 82,83 These examples are just a few improvements in the ®eld of ultrasound. Other research and new technologies will help the physician in interpreting the information of the ultrasound images.…”

Section: Future Prospectives Of Transrectal Ultrasonographymentioning

confidence: 99%

Transrectal ultrasound imaging of the prostate: review and perspectives of recent developments

Sedelaar

Rosette

Beerlage

et al. 1999

Prostate Cancer Prostatic Dis

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We present a critical review of the recent literature and discuss the development and prospective view of the evaluations of transrectal ultrasound with regard to prostate malignancy. We illustrate this with personal experiences. Material and Methods: Based on a critical evaluation of clinical data we address the apparent shortcoming of greyscale transrectal ultrasonography in the assessment of prostate cancer. New developments and future possibilities are also discussed.Evaluation of the value of greyscale transrectal ultrasonography in the diagnosis of prostate cancer indicates a limited role, because of the non-uniform appearance of prostate cancer on the ultrasound images. Ameliorating of transrectal ultrasound, like the use of contrast ultrasonography, could improve the detection of prostate cancer.Although the use of greyscale transrectal ultrasonography lacks sensitivity and speci®city for the diagnosis of prostate cancer, its use in volume measurement of the prostate and biopsy guidance is unquestionable. The ®rst results of the application of contrast ultrasonography are promising, both in detection of prostate cancer as in treatment follow up. Other developments like improvement of transducer and computer technology could make the use of ultrasound more versatile. However, future research will indicate whether all these improvements will lead to clinical applications.

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“…Aarnink further developed his idea and implemented edge detection in conjunction with a maximum and minimum filter [6]. Prater also used neural network to segment prostate [7].…”

Section: Introductionmentioning

confidence: 99%

Using Wavelet and Bayesian Decision Theory in Real-Time Prostate Volume Measurements

Nia

2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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Abstract-The volume of prostate is one of the key indicators in the diagnosis and treatment of prostate cancer. This paper presents a novel method to calculate the volume of prostate in MRI images with high accuracy and in real time. In this approach, wavelet transform is used to decompose a MRI image into spatially oriented channels and then decompose each sub-image into 1D signal, by obtaining integral of subimages. Bayesian decision theory is then used to analyze signals and detect the boundaries of prostate. Experimental results show that the proposed method can be implemented in real time and has acceptable accuracy.

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Edge detection in prostatic ultrasound images using integrated edge maps

Cited by 55 publications

References 14 publications

A survey of prostate modeling for image analysis

A survey of prostate modeling for image analysis

Transrectal ultrasound imaging of the prostate: review and perspectives of recent developments

Using Wavelet and Bayesian Decision Theory in Real-Time Prostate Volume Measurements

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