Segmentation of prostate boundaries using regional contrast enhancement

Sahba, Farhang; Tizhoosh, Hamid R.; Salama, M.M.A.

doi:10.1109/icip.2005.1530293

Cited by 10 publications

(18 citation statements)

References 5 publications

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“…The algorithm used in the present study to identify the prostate body region is based on the prostate center seeking algorithm presented in [8].…”

Section: Region Identificationmentioning

confidence: 99%

“…Different methods of delineating the prostate, such as those presented in [3,5,7,8], have shown to be successful in segmenting the prostate, however still requiring the input of the doctor to identify the prostate. The present study presents an algorithm of automatically identifying the prostate within the TRUS image.…”

Section: Introductionmentioning

confidence: 99%

“…Computer aided segmentation leads to faster, and more accurate and precise results, without much interference from the doctor, [7]. Segmentation of the prostate within the ultrasound image can be further used for real-time biopsy guidance, automatic volume determination, and computer automated analysis of the prostate for cancer and abnormal regions.Different methods of delineating the prostate, such as those presented in [3,5,7,8], have shown to be successful in segmenting the prostate, however still requiring the input of the doctor to identify the prostate. The present study presents an algorithm of automatically identifying the prostate within the TRUS image.…”

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confidence: 99%

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Prostate ultrasound image processing

Stefan¹

2007

XRDS

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IntroductionProstate cancer is the second most common cancer found in men in the United States and a leading factor in cancer-related deaths. It is a major health issue causing a dramatic impact on the lifestyle of elderly men. Treatment is, however, available, but most successful when the cancer is in an early stage; treatment of an advanced cancer, especially if the spread is beyond the prostate, can be very complex and in many cases incurable. To find the cancer still in an early stage it important for men over the age of 50 to get an annual screening. Presently, several different screening techniques are used to diagnose the presence of the prostate carcinoma, the most common of which are digital rectal examination (DRE), prostate specific antigen (PSA) test and transrectal ultrasound (TRUS). In cases where the presence of cancer is highly suspected, a biopsy usually follows the screening to fully confirm the diagnosis.The DRE is a procedure in which the doctor checks the size, shape, and texture of the prostate by inserting a finger into the rectum of the patient. This procedure is not accurate because only the back wall of the prostate is examined (the middle and front of the prostate remain undiagnosed) and only if the cancer is in an already advanced stage, will a noticeable difference in the texture be recognized.The PSA test, which commonly accompanies the DRE, measures the amount of prostate specific antigen in the blood. A high PSA level usually indicates the presence of cancer, however, because common diseases and infections of the prostate, such as benign prostatic hypertrophy (BPH) and prostatitis, cause an elevation in the PSA level the accuracy of the diagnosis cannot be determined. Furthermore, many patients with normal PSA levels were later diagnosed with cancer. TRUS is the most common medical imaging modality used in the screenings, followed by X-ray computer axial tomography (CAT) which is used to diagnose the spread of the cancer beyond the prostate. TRUS is inexpensive, provides the radiologist with enough detail to recognize any abnormalities within the prostate, and can also be used in guiding a biopsy in real-time. TRUS usually accompanies the DRE and PSA test, because it does not provide enough detail to be used as the only screening technique in the diagnosis.In a typical TRUS screening, the prostate is manually delineated in the ultrasound image to calculate its size and volume, which are additional details used to support the diagnosis. Because manual segmentation of the prostate is very tedious and usually prolongs the diagnosis, automated delineation of the prostate is necessary. Computer aided segmentation leads to faster, and more accurate and precise results, without much interference from the doctor, [7]. Segmentation of the prostate within the ultrasound image can be further used for real-time biopsy guidance, automatic volume determination, and computer automated analysis of the prostate for cancer and abnormal regions.Different methods of delineating the prostate, such as t...

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“…The algorithm used in the present study to identify the prostate body region is based on the prostate center seeking algorithm presented in [8].…”

Section: Region Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Prostate ultrasound image processing

Stefan¹

2007

XRDS

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“…By transforming prostate image into polar space, Zwiggelaar et al segment the prostate by line detection and non-maximum suppression [18]. Sahba et al [19,20] use locally adaptive enhancement [21] and Kalman filter to segment the prostate gland. Rahnamyan et al [22] focus on automated initialization of snakes, whereas Bustince et al [23] use ignorance functions for prostate thresholding.…”

Section: Prostate Segmentationmentioning

confidence: 99%

Multi-Resolution Level Sets with Shape Priors: A Validation Report for 2D Segmentation of Prostate Gland in T2W MR Images

2014

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The level set approach to segmentation of medical images has received considerable attention in recent years. Evolving an initial contour to converge to anatomical boundaries of an organ or tumor is a very appealing method, especially when it is based on a well-defined mathematical foundation. However, one drawback of such evolving method is its high computation time. It is desirable to design and implement algorithms that are not only accurate and robust but also fast in execution. Bresson et al. have proposed a variational model using both boundary and region information as well as shape priors. The latter can be a significant factor in medical image analysis. In this work, we combine the variational model of level set with a multi-resolution approach to accelerate the processing. The question is whether a multi-resolution context can make the segmentation faster without affecting the accuracy. As well, we investigate the question whether a premature convergence, which happens in a much shorter time, would reduce accuracy. We examine multiple semiautomated configurations to segment the prostate gland in T2W MR images. Comprehensive experimentation is conducted using a data set of a 100 patients (1,235 images) to verify the effectiveness of the multi-resolution level set with shape priors. The results show that the convergence speed can be increased by a factor of ≈ 2.5 without affecting the segmentation accuracy. Furthermore, a premature convergence approach drastically increases the segmentation speed by a factor of ≈17.9.

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“…Also, Sahba et al [81] proposed an automated technique that used morphological information [82], a Kalman estimator [83], and fuzzy inference to extract the edges of the prostate. The main limitation of edge detection techniques is that they do not work well with noisy images and/or objects with unclear or diffused edges.…”

Section: A In-vitro Prostate Cancer Diagnostic Technologiesmentioning

confidence: 99%

A novel NMF-based DWI CAD framework for prostate cancer.

McClure¹

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Segmentation of prostate boundaries using regional contrast enhancement

Cited by 10 publications

References 5 publications

Prostate ultrasound image processing

Prostate ultrasound image processing

Multi-Resolution Level Sets with Shape Priors: A Validation Report for 2D Segmentation of Prostate Gland in T2W MR Images

A novel NMF-based DWI CAD framework for prostate cancer.

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