Predicting Malignant Cancer Using Machine Learning

One of the primary concerns that is also a demanding issue within the realm of medical specialism is the detection and removal of tumours. Because visualisation approaches had the drawback of being adversarial, doctors relied heavily on MRI images to provide a superior result. Pre-processing, tumour segmentation, and tumour operations are the three stages in which tumour image processing takes place. Following the acquisition of the source image, the original image is converted to grayscale. Additionally, a noise removal filter and a median filter for quality development are provided, followed by an exploration stage that yields hits orgasmic identical images. Finally, the watershed algorithm is used to complete the segmentation. This proposed methodology is useful in automatically organising reports in a short amount of time, and exploration has resulted in the removal of many less tumour parameters. Keywords: MRI Imaging, Segmentation, Watershed Algorithm.

Automatic segmentation of foveal avascular zone based on adaptive watershed algorithm in retinal optical coherence tomography angiography images

The size and shape of the foveal avascular zone (FAZ) have a strong positive correlation with several vision-threatening retinovascular diseases. The identification, segmentation and analysis of FAZ are of great significance to clinical diagnosis and treatment. We presented an adaptive watershed algorithm to automatically extract FAZ from retinal optical coherence tomography angiography (OCTA) images. For the traditional watershed algorithm, “over-segmentation” is the most common problem. FAZ is often incorrectly divided into multiple regions by redundant “dams”. This paper analyzed the relationship between the “dams” length and the maximum inscribed circle radius of FAZ, and proposed an adaptive watershed algorithm to solve the problem of “over-segmentation”. Here, 132 healthy retinal images and 50 diabetic retinopathy (DR) images were used to verify the accuracy and stability of the algorithm. Three ophthalmologists were invited to make quantitative and qualitative evaluations on the segmentation results of this algorithm. The quantitative evaluation results show that the correlation coefficients between the automatic and manual segmentation results are 0.945 (in healthy subjects) and 0.927 (in DR patients), respectively. For qualitative evaluation, the percentages of “perfect segmentation” (score of 3) and “good segmentation” (score of 2) are 99.4% (in healthy subjects) and 98.7% (in DR patients), respectively. This work promotes the application of watershed algorithm in FAZ segmentation, making it a useful tool for analyzing and diagnosing eye diseases.

Experimental Discussion on Fire Image Recognition Based on Feature Extraction

Video image-based fire detection technology can overcome some shortcomings of traditional fire detection, and has a good development prospect. This paper summarizes the basic principles of image-based fire detection, and analyzes the main features of fire combustion images. According to these features, firstly, the interframe difference method and the watershed algorithm are used to extract the suspected fire image area which may occur. Then, the features of flame image in early fire stage, such as increasing flame area, fluttering edge, irregular shape and flame color, are used as fire recognition criteria. Meanwhile, various image processing technologies and algorithms are used to extract the four main features of the fire, so as to eliminate various sources of interference and further determine whether a fire has occurred. Finally, a variety of different fuels were selected under indoor conditions to simulate fire experiments under different conditions, and the video was recorded. Fire recognition experiments were carried out using experimental videos and some videos found on the Internet. The experimental results show that the extraction and further recognition of suspected fire areas are both effective. However, the experimental simulation environment is relatively simple, and many theoretical and practical problems need to be further studied and solved.

Hand measurement and analysis based on image and mark watershed algorithm

PurposeThe purpose of this paper is to propose a method for hand measurement based on image and marker watershed algorithm, and combine the data to analyze the shape and characteristics of the hand.Design/methodology/approachA portable hand image capturing instrument was designed and manufactured, and the hand images and dimensions of 328 young men in Zhejiang area were obtained. The outer contour curve of the hand and the key points of finger root, fingertip, wrist and knuckle position were extracted. Then, the size of each hand part was calculated. The hand data obtained from the two-dimensional image was compared with the manual measurement data. Finally, the hands were classified according to the measurement data, and the relationship between hand control size and hand length, hand width and the relationship between hand length and height were explored.FindingsThe data comparison results show that the two measurement methods have high data consistency and are replaceable. In addition, analyzing the data obtained four major characteristic factors that affect the shape of the hand, divided the hands of young men in Zhejiang into five categories, and obtained the regression equations of basic hand size, hand length and hand width, and obtained the regression equation of hand length and height.Originality/valueThe method proposed in this study to obtain hand size based on the image and mark watershed algorithm has lower requirements on the external environment and testers, conforms to the development trend of applying artificial intelligence to anthropometric engineering and provides a useful reference value for data collection of gloves specification design. In addition, the results of data analysis can provide a valuable reference basis for consumer hand shape predictions, which can be used to guide the research and production of hand instruments, the design of specifications series and the purchase of hand products.

CONSTRUÇÃO DE IMAGENS PANORÂMICAS EM MÚLTIPLAS FAIXAS DE ALTURA E LARGURA USANDO ALGORITMOS WATERSHED E GRAPH-CUT

In this work we developed an algorithm for building panoramas with multiple height and width ranges. For multilinear stitching, images were initially placed in a matrix and partial panoramas with images from the same column are generated. To complete the final panorama, the columns were divided with the help of support points and the neighboring columns were stitched, being reassembled at the end of the process. The stitching was performed with a graph cut algorithm in conjunction with the Watershed algorithm.

Integration of Dynamic Multi-Atlas and Deep Learning Techniques to Improve Segmentation of the Prostate in MR Images

Accurate delineation of the prostate in MR images is an essential step for treatment planning and volume estimation of the organ. Prostate segmentation is a challenging task due to its variable size and shape. Moreover, neighboring tissues have a low-contrast with the prostate. We propose a robust and precise automatic algorithm to define the prostate’s boundaries in MR images in this paper. First, we find the prostate’s ROI by a deep neural network and decrease the input image’s size. Next, a dynamic multi-atlas-based approach obtains the initial segmentation of the prostate. A watershed algorithm improves the initial segmentation at the next stage. Finally, an SSM algorithm keeps the result in the domain of allowable prostate shapes. The quantitative evaluation of 74 prostate volumes demonstrated that the proposed method yields a mean Dice coefficient of [Formula: see text]. In comparison with recent researches, our algorithm is robust against shape and size variations.

Droplets Image Segmentation Method Based on Machine learning and Watershed

Watershed algorithm is used widely in segmentation of droplet overlapped spots on water-sensitive test paper. However, the phenomenon of over-segmentation, however, is often caused by noise and subtle changes of gray levels in images. To further improve segmentation accuracy of watershed algorithm, this paper proposes a cyclic iterative watershed segmentation algorithm. Through statistical analysis and logistic regression, machine learning models were classified to extract overlapping droplets on test papers. Loop iterative processing of seed points segments overlapping droplets with appropriate thresholds. Compared with fixed threshold watershed segmentation, this method has higher precision and efficiency for spray droplet evaluation in pesticide application.