Quantitative Volumetric K-Means Cluster Segmentation of Fibroglandular Tissue and Skin in Breast MRI

Niukkanen, Anton; Arponen, Otso; Nykänen, Aki; Masarwah, Amro; Sutela, Anna; Liimatainen, Timo; Vanninen, Ritva; Sudah, Mazen

doi:10.1007/s10278-017-0031-1

Cited by 15 publications

(6 citation statements)

References 37 publications

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“…On the other hand, there are no reports on lung field segmentation using cine MRI and the k-means method. Conventionally, MRI images of the head [35] , [36] and mammary gland [37] have been segmented using the k-means method. The k-means method is now used for segmenting MRI images because the number of clusters is usually determined according to each region in the human body [38] .…”

Section: Discussionmentioning

confidence: 99%

Semiautomatic assessment of respiratory dynamics using cine MRI in chronic obstructive pulmonary disease

Sato

Kawata

Shimada³

et al. 2022

European Journal of Radiology Open

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Section: Discussionmentioning

confidence: 99%

Semiautomatic assessment of respiratory dynamics using cine MRI in chronic obstructive pulmonary disease

Sato

Kawata

Shimada³

et al. 2022

European Journal of Radiology Open

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“…Subsequently, the distance between each object and each

m

is evaluated and allotted to the near cluster. The mentioned process is repeated until the final criterion gets converged 31 . The derived features from BOW are pointed out as

{Fe}_{bw}

.…”

Section: Extraction Of Lbp Features Bags Of Visual Words and Proposed...mentioning

confidence: 99%

“…The mentioned process is repeated until the final criterion gets converged. 31 The derived features from BOW are pointed out as Fe bw .…”

Section: Bag Of Visual Wordsmentioning

confidence: 99%

Proposed spatio‐temporal features for human activity classification using ensemble classification model

Tyagi

Singh

Dev

2023

Concurrency and Computation

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Classifying human actions from still images or video sequences is a demanding task owing to issues, like lighting, backdrop clutter, variations in scale, partial occlusion, viewpoint, and appearance. A lot of appliances, together with video systems, human-computer interfaces, and surveillance necessitate a compound action recognition system. Here, the proposed system develops a novel scheme for HAR. Initially, filtering as well as background subtraction is done during preprocessing. Then, the features including local binary pattern (LBP), bag of the virtual word (BOW), and the proposed local spatio-temporal features are extracted. Then, in the recognition phase, an ensemble classification model is introduced that includes Recurrent Neural networks (RNN 1 and RNN 2) and Multi-Layer Perceptron (MLP 1 and MLP 2). The features are classified using RNN 1 and RNN 2, and the outputs from RNN 1 and RNN 2 are further classified using MLP 1 and MLP 2, respectively. Finally, the outputs attained from MLP 1 and MLP 2 are averaged and the final classified output is obtained. At last, the superiority of the developed approach is proved on varied measures. K E Y W O R D Saccuracy, human action, multi-layer perceptron, proposed spatio-temporal features, RNN INTRODUCTION"HAR is defined as a classification task where the movement of a person is categorized based on data received from different sources such as sensors, cameras, etc.". It has different appliances in healthcare, particularly in constant action monitoring and fall recognition of aged persons. [1][2][3] In addition, it has vast prospective to support emerging appliances namely, augmented reality, IoT, and indoor localization in smart construction controlling systems to offer a secure indoor surrounding with higher EE.Diverse studies were carried out on HAR techniques in the state of the works using computer vision, ambient devices, sensors, and smartphones. These approaches are usually classified into two classes, that is, passive and active monitoring schemes. [4][5][6] In an AMS, exterior devices like wearable sensors and cameras were deployed. Sensor-based HAR is extensively deployed in analysis, in which a person has to continually carry sensors such as gyroscope, pedometer, and accelerometer that can turn out to be difficult for the person in numerous cases, particularly for elder ones. [7][8][9]

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“…Thus, determining the exact number of clusters is difficult. In some of the previous studies by Niukkanen et al (2018) and Rezaee and Haddadnia (2013), the number of clusters for breast lesion segmentation using KM was chosen empirically to be k = 4-6, k = 5, k = 4-7, k = 3, 4 and k = 6, respectively. In KM, the k value varies for images generated from different mammographic scanners.…”

Section: Connected Component Analysismentioning

confidence: 99%

Segmentation of malignant tumours in mammogram images: A hybrid approach using convolutional neural networks and connected component analysis

et al. 2021

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The segmentation of breast lesions is an important step in the computer-aided analysis of the mammogram. The presence of noise in mammograms makes lesion detection challenging particularly for complex malignant lesions. Pre-processing techniques can deal with the noise issue but distorts the important shape features. This motivates us to propose a novel hybrid approach by combining a convolution neural network (CNN) with connected component analysis (CCA) to segment malignant breast lesions without any pre-processing to avoid any distortion in image sharpness at the initial stages. Two well-known segmentation techniques namely, K-means (KM) and Fuzzy c-means (FCM) are also used to compare the results. From a pool of 1045 mammographic cancer images acquired from the Digital Database for Screening Mammography (DDSM), 1016 are used for training and validation, and 29 are used for testing. All three results (Hybrid, KM and FCM) are compared against the results by the expert Radiologist. The results indicate that, among various segmentation techniques, the proposed hybrid approach achieves the highest accuracy (90%), Matthew's correlation coefficient (0.79), Jaccard index (0.73) and the Dice similarity coefficient (0.84). Other performance evaluation techniques such as; precision, sensitivity, specificity, false-positive rate, false discovery rate, negative predictive value and false-negative rate also show the superior performance of the proposed hybrid approach. Statistical analysis (Mann-Whitney U test, T-test, Chi-square test, Kolmogorov-Smirnov test and Wilcoxon test), graphical analysis (Regression and Bland-Altman plots) and receiver operating characteristic curve further demonstrate the stability and consistency of the results.

show abstract

Quantitative Volumetric K-Means Cluster Segmentation of Fibroglandular Tissue and Skin in Breast MRI

Cited by 15 publications

References 37 publications

Semiautomatic assessment of respiratory dynamics using cine MRI in chronic obstructive pulmonary disease

Semiautomatic assessment of respiratory dynamics using cine MRI in chronic obstructive pulmonary disease

Proposed spatio‐temporal features for human activity classification using ensemble classification model

Segmentation of malignant tumours in mammogram images: A hybrid approach using convolutional neural networks and connected component analysis

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