A Comparative Study of Thresholding Algorithms on Breast Area and Fibroglandular Tissue

Uyun, Shofwatul; Hartati, Sri; Harjoko, Agus; Choridah, Lina

doi:10.14569/ijacsa.2015.060117

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…First, we apply adaptive thresholding to the input image. The adaptive threshold is determined based on the brightness histogram of the ROI of the image, as well as the image-binarization algorithm [ 62 ]. There is also a need for noise to be removed in the image after thresholding.…”

Section: Proposed Methodsmentioning

confidence: 99%

Remote Marker-Based Tracking for UAV Landing Using Visible-Light Camera Sensor

Nguyen-Ha

Kim

Lee

et al. 2017

Sensors

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Unmanned aerial vehicles (UAVs), which are commonly known as drones, have proved to be useful not only on the battlefields where manned flight is considered too risky or difficult, but also in everyday life purposes such as surveillance, monitoring, rescue, unmanned cargo, aerial video, and photography. More advanced drones make use of global positioning system (GPS) receivers during the navigation and control loop which allows for smart GPS features of drone navigation. However, there are problems if the drones operate in heterogeneous areas with no GPS signal, so it is important to perform research into the development of UAVs with autonomous navigation and landing guidance using computer vision. In this research, we determined how to safely land a drone in the absence of GPS signals using our remote maker-based tracking algorithm based on the visible light camera sensor. The proposed method uses a unique marker designed as a tracking target during landing procedures. Experimental results show that our method significantly outperforms state-of-the-art object trackers in terms of both accuracy and processing time, and we perform test on an embedded system in various environments.

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Section: Proposed Methodsmentioning

confidence: 99%

Remote Marker-Based Tracking for UAV Landing Using Visible-Light Camera Sensor

Nguyen-Ha

Kim

Lee

et al. 2017

Sensors

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“…1 The proposed model for handwriting recognition  Adaptive threshold. Thresholding is a good segmentation technique for images with a significant difference in intensity values between the background and the main object [22]. In its application, thresholding requires a value used as the limiting value between the main object and the background, called the threshold [23].…”

Section: B Pre-processingmentioning

confidence: 99%

“…At this classification stage, the testing data type of character is identified based on the model resulting from the previous training process using a classification algorithm. This research uses a deep learning algorithm, especially a convolutional neural network [12], [22]. CNN is the Multilayer Perceptron (MLP) development designed to process two-dimensional data.…”

Section: Classificationmentioning

confidence: 99%

Skew Correction and Image Cleaning Handwriting Recognition Using a Convolutional Neural Network

Uyun,

Rahardyan,

Anshari

2023

JOIV : Int. J. Inform. Visualization

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Handwriting recognition is a study of Optical Character Recognition (OCR) which has a high level of complexity. In addition, everyone has a unique and inconsistent handwriting style in writing characters upright, affecting recognition success. However, proper pre-processing and classification algorithms affect the success of pattern recognition systems. This paper proposes a pre-processing method for handwriting image recognition using a convolutional neural network (CNN). This study uses public datasets for training and private datasets for testing. This pre-processing consists of three processes: image cleaning, skew correction, and segmentation. These three processes aim to clean the image from unnecessary ink streaks. In addition, to make angle corrections to characters in italics in their writing. The model testing process uses image test data of handwriting that are not straight. There are three images based on the inclination angle: less than 45 degrees, equal to 45 degrees, and more than 45 degrees. Picture cleaning removes unnecessary strokes (noise) from the image using a layer mask, whereas skew correction changes the handwriting to an upright posture based on the detected angle. The pre-processing model we propose worked optimally on handwriting with a skew angle of fewer than 45 degrees and 45 degrees. Our proposed model generally works well for handwriting with fewer than 45 degrees skew with an accuracy of 88,96%. Research with a similar scope can continue to improve optimization with a focus on algorithms related to analysis layout studies. Besides that, it can focus more on automation in the segmentation process of each character.

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“…The thresholding method can identify and extract the region of interest, such as the foreground (fibroglandular tissue) from its background (adipose tissue) based on the texture (grey-level distribution) in image areas. An example is based on entropy distribution of the grey levels in mammographic images, which after the ratio value between the foreground and background is quantified after thresholding [19,20]. The ratio of the radiodense (white) area over the entire breast area is known as the quantified MBD.…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution and Quantification of Mammographic Breast Density, and Its Correlation with BI-RADS Using an Image Segmentation Method

Goh

Lee

Lai

2021

Life

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(1) Background: Mammographic breast density (MBD) and older age are classical breast cancer risk factors. Normally, MBDs are not evenly distributed in the breast, with different women having different spatial distribution and clustering patterns. The presence of MBDs makes tumors and other lesions challenging to be identified in mammograms. The objectives of this study were: (i) to quantify the amount of MBDs—in the whole (overall), different sub-regions, and different zones of the breast using an image segmentation method; (ii) to investigate the spatial distribution patterns of MBD in different sub-regions of the breast. (2) Methods: The image segmentation method was used to quantify the overall amount of MBDs in the whole breast (overall percentage density (PD)), in 48 sub-regions (regional PDs), and three different zones (zonal PDs) of the whole breast, and the results of the amount of MBDs in 48 sub-regional PDs were further analyzed to determine its spatial distribution pattern in the breast using Moran’s I values (spatial autocorrelation). (3) Results: The overall PD showed a negative correlation with age (p = 0.008); the younger women tended to have denser breasts (higher overall PD in breasts). We also found a higher proportion (p < 0.001) of positive autocorrelation pattern in the less dense breast group than in the denser breast group, suggesting that MBDs in the less dense breasts tend to be clustered together. Moreover, we also observed that MBDs in the mature women (<65 years old) tended to be clustered in the middle zone, while in older women (>64 years old) they tended to be clustered in both the posterior and middle zones. (4) Conclusions: There is an inverse relationship between the amount of MBD (overall PD in the breast) and age, and a different clustering pattern of MBDs between the older and mature women.

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A Comparative Study of Thresholding Algorithms on Breast Area and Fibroglandular Tissue

Cited by 5 publications

References 14 publications

Remote Marker-Based Tracking for UAV Landing Using Visible-Light Camera Sensor

Remote Marker-Based Tracking for UAV Landing Using Visible-Light Camera Sensor

Skew Correction and Image Cleaning Handwriting Recognition Using a Convolutional Neural Network

Spatial Distribution and Quantification of Mammographic Breast Density, and Its Correlation with BI-RADS Using an Image Segmentation Method

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