Automatic Segmentation of Brachial Artery based on Fuzzy C-Means Pixel Clustering from Ultrasound Images

Park, Joonsung; Song, Doo Heon; Nho, Hosung; Hyun-Min, Choi; Kim, Kyung-Ae; Park, Hyun Jun; Kim, Kwang-Baek

doi:10.11591/ijece.v8i2.pp638-643

Cited by 13 publications

(18 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(6) as explained in section 2.2. (28) and J2 is adaptive complex diffusion based non linear filter adapted to Rayleigh noise, as explained in section 2.3, which is responsible for reduction of Rayleigh noise from segmented image in each iteration obtained by minimizing modified cost functional of modified FCM algorithm as mentioned above in Eq. (24).…”

Section: The Proposed Model For Segmentation Of Ultrasound Image In Pmentioning

confidence: 99%

See 1 more Smart Citation

A Complex Diffusion Based Modified Fuzzy C- Means Approach for Segmentation of Ultrasound Image in Presence of Speckle Noise for Breast Cancer Detection

Srivastava¹,

Kumar²,

Mishra³

et al. 2020

RIA

View full text Add to dashboard Cite

This paper proposes a single framework for segmentation of abnormalities for breast cancer detection from Ultrasound images in presence of Rayleigh noise i.e. noise removal and segmentation are embedded in single step. It accomplishes dual purpose in a single framework simultaneously for the preprocessing and segmentation. The proposed framework comprises of two terms, first term, is used for segmentation which is a modified fuzzy c-means segmentation (MFCM) approach while second term is an adaptive complex diffusion based non linear filter (ACDPDE) that performs as regularization function for removal of Rayleigh noise, enhancement, and edge preservation of ultrasound Image. The various existing segmentation methods viz. K-Means, Texture based, Fuzzy C-Means (FCM), total variation based FCM (TVFCM), Adaptive fourth order PDE based FCM (AFPDEFCM), and the proposed method are evaluated for 50 sample ultrasound images of breast cancer. The region of interest (ROI) segmented image of ultrasound breast tissue is compared with ground truth images. From the acquired results and its analysis, it is observed that the proposed method is more robust and provides better segmentation result for ultrasound images in terms of various performance measures such as Global Constancy error (GCE), Tanimoto coefficient, Variation of Information (VOI), Probability Random Index (PRI), Jaccard coefficient, accuracy, True Positive Rate (TPR), False Positive Rate (FPR), True Negative Rate (TNR), dice index, False Negative Rate (FNR), and Area under curve (AUC). The proposed approach is capable of handling segmentation problem of blocky artifacts while achieving good tradeoff between Rayleigh noise removal and edge preservation. The proposed method may be useful for finding additional 33% cases of breast cancer which is missed or not detected by mammography.

show abstract

Section: The Proposed Model For Segmentation Of Ultrasound Image In Pmentioning

confidence: 99%

“…K means is found to be a better option for exclusive clustering but does not use local spatial statistics of the pixels. Fuzzy c means [22][23][24][25][26][27][28] is a soft clustering method where the division of image into clusters is based on membership function. But FCM method is found to be sensitive to noise.…”

Section: Introductionmentioning

confidence: 99%

A Complex Diffusion Based Modified Fuzzy C- Means Approach for Segmentation of Ultrasound Image in Presence of Speckle Noise for Breast Cancer Detection

Srivastava¹,

Kumar²,

Mishra³

et al. 2020

RIA

View full text Add to dashboard Cite

show abstract

“…From the process result of Log amplifier above, it obtained an electric quantity representing µ value of aluminum filter on X-ray intensity, which the intensity produced depends on tube voltage score. Measuring the electric quantity, it was later inputted into pin ADC Arduino Gelatino with IC Atmega 16 [19]. Subsequently, the electric quantity representing µ value was converted into voltage in millivolt (mV).…”

Section: Systemmentioning

confidence: 99%

Kilovolt Peak Meter Design as a Calibrator of X-Ray Machine

Fajrin¹,

Rahmat²,

Sukwono³

2019

IJECE

View full text Add to dashboard Cite

This research aimed to design the KvP meter for checking the error of output voltage from X-Ray machine with the range of 50-70 KvP. It used Arduino Gelatino microcontroller programmed with Arduino IDE software as data processor of detector result. The detector was designed based on the physics principle using material attenuation coefficient namely aluminum whose good effect in reducing the energy level of X-ray photons. Thus, the photodiode sensor only read the effective energy of the X-ray energy. Based on the radiation intensity ratio through the aluminum filter with 0.25 mm and 0.5 mm thickness, it is better to measure X-ray tube voltage non-invasively The KvP Meter was tested by setting duration of X-ray exposure that was 0.3 second, tube current was 10 mA, and KvP Meter distance from collimator was 90 cm. A prototype unit was made, and the performance was tested in terms of error and precision. After testing and analyzing the data, the error result was less than 5.1% with the highest measurement precision of ± 1.50. It is generally concluded that this equipment can be used to measure the voltage of the X-ray tube.

show abstract

“…Instead, pixel clustering approaches have been successfully applied to detect the target organ from ultrasonography or X-ray images. Some examples of such approaches include detecting brain tumor [13,14], brachial artery [15], cervical vertebrae [16], lung cancer [17], inflamed appendix [18], ganglion cyst [19] and breast image segmentation [20]. Thus, in this paper, we take K-means based pixel clustering approach [16,21] to this automatic segmentation problem.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of wrist bone fracture area by K-means pixel clustering from X-ray image

Kim¹,

Song²,

Yun³

2019

IJECE

Self Cite

View full text Add to dashboard Cite

Early detection of subtle fracture is important particularly for the senior citizens’ quality of life. Naked eye examination from X-ray image may cause false negatives due to operator subjectivity thus computer vision based automatic detection software is much needed in practice. In this paper, we propose an automatic extraction method for suspisious wrist fracture regions. We apply K-means in pixel clustering to form the candidate part of possible fracture from wrist X-ray image automatically. This method can recover previously detected patterned false cases with edge detection method after fuzzy stretching. The proposed method is successful in 16 out of 20 tested cases in experiment.

show abstract

Automatic Segmentation of Brachial Artery based on Fuzzy C-Means Pixel Clustering from Ultrasound Images

Cited by 13 publications

References 9 publications

A Complex Diffusion Based Modified Fuzzy C- Means Approach for Segmentation of Ultrasound Image in Presence of Speckle Noise for Breast Cancer Detection

A Complex Diffusion Based Modified Fuzzy C- Means Approach for Segmentation of Ultrasound Image in Presence of Speckle Noise for Breast Cancer Detection

Kilovolt Peak Meter Design as a Calibrator of X-Ray Machine

Automatic segmentation of wrist bone fracture area by K-means pixel clustering from X-ray image

Contact Info

Product

Resources

About