Segmentation of breast tumors in mammograms by fuzzy region growing

Guliato, Denise; Rangayyan, Rangaraj M.; Carnielli, Walter; Zuffo, João Antonio; Desautels, J. E. Leo

doi:10.1109/iembs.1998.745618

Cited by 37 publications

(25 citation statements)

References 10 publications

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“…We use fuzzy function proposed in [12] within a new form to provide a non-linear adjustment to the image based on the image gray level statistics. The fuzzy function in [12] attributes a membership value between 0 and 1 to the pixels based on the difference of their gray levels than a seed point, located at the mass core.…”

Section: Preprocessing Filter: Fclahementioning

confidence: 99%

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A New Preprocessing Filter for Digital Mammograms

Rahmati

Hamarneh

Nussbaum

et al. 2010

Lecture Notes in Computer Science

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Abstract. This paper presents a computer-aided approach to enhancing suspicious lesions in digital mammograms. The developed algorithm improves on a well-known preprocessor filter named contrast-limited adaptive histogram equalization (CLAHE) to remove noise and intensity inhomogeneities. The proposed preprocessing filter, called fuzzy contrast-limited adaptive histogram equalization (FCLAHE), performs non-linear enhancement. The filter eliminates noise and intensity inhomogeneities in the background while retaining the natural gray level variations of mammographic images within suspicious lesions. We applied Catarious segmentation method (CSM) to compare the segmentation accuracy in two scenarios: when there is no preprocessing filter, and when the proposed preprocessing filter is applied to the original image. The proposed filter has been evaluated on 50 real mammographic images and the experimental results show an average increase of segmentation accuracy by 14.16% when the new filter is applied.

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Section: Preprocessing Filter: Fclahementioning

confidence: 99%

“…The fuzzy function in [12] attributes a membership value between 0 and 1 to the pixels based on the difference of their gray levels than a seed point, located at the mass core. The fuzzy function defined as [12]:…”

Section: Preprocessing Filter: Fclahementioning

confidence: 99%

A New Preprocessing Filter for Digital Mammograms

Rahmati

Hamarneh

Nussbaum

et al. 2010

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Fig. 12 depicts the final delineation contour of MLACMLS for the minimum and the maximum value of β for two mammographic images chosen from the two different data bases (DDSM and the images reproduced from Guliato et al (2003aGuliato et al ( , 2003b). Fig.…”

Section: Robustness To Changes In Fclache's Openness Parametermentioning

confidence: 99%

“…We integrate these steps into the FCLAHE algorithm as follows. We use the fuzzy function proposed by Guliato et al (2003a) within a new form to provide a non-linear adjustment to the image based on its gray level statistics. The fuzzy function assigns a membership value between 0 and 1 to the pixels based on the difference between their gray levels and a seed point, located at the mass core.…”

Section: Preprocessing Filter (Fclahe)mentioning

confidence: 99%

“…The fuzzy function assigns a membership value between 0 and 1 to the pixels based on the difference between their gray levels and a seed point, located at the mass core. The fuzzy function is defined as (Guliato et al, 2003a): where p is the intensity of the pixel being processed, d is the intensity difference between p and a seed point, and β controls the opening of the function. The larger the difference, the lower the membership; and as β increases, the opening of the F(p) decreases.…”

Section: Preprocessing Filter (Fclahe)mentioning

confidence: 99%

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