Detection of Uterus Fibroids in Ultrasound Images: a survey · 2018-03-15 · Detection of Uterus Fibroids in Ultrasound Images: a survey Dilna K T1 and D.Jude Hemanth2 1Department

Detection of Uterus Fibroids inUltrasound Images: a survey

Dilna K T1 and D.Jude Hemanth2

1Department of ECE,College of engineering and Technology,Payyanur

[email protected] of ECE,Karunya University

January 10, 2018

Abstract

Uterine fibroids are an abnormal growth of smooth mus-cle in the wall of the uterus. These growths are benigntumors that occur as a single large mass or many smallermasses. Although it is very rare, this can become malignant(cancerous) and the presence of fibroid can cause infertilityand repeated miscarriage. Ultrasound images are an appro-priate tool for diagnosing uterus related disorders. Extract-ing the fibroid from ultrasound scanned uterus image is achallenging task because of size, location and low contrastboundaries. In this paper, a study of various ultrasoundimage segmentation and uterus fibroid detection methods isgiven. A survey on detection of fibroid is clarified with acomparative study.

Key Words :Fibroid, Uterus, Ultrasonic Imaging, seg-mentation

1 Introduction

The uterus is the part of the female reproductive system. A normaluterus is about 7.5cm (3in) long, 5cm (2in) wide and 2.5cm (1in)deep. Inside, it is hollow with thick muscular walls. There are

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a few abnormalities present in the uterus such as tumors. Thesetumors can be benign or malignant in nature which is called asFibroid. These are also known as uterine myomas, leiomyomas, orfibromas. Fibroids are muscular tumors that grow in the wall of theuterus. Fibroids are almost always benign (not cancerous). Typesof fibroid: Fibroids can be classified according to their position inthe uterus or womb:

a. ubserosal - towards the outside of the womb / uterus

b. Intramural - in the wall of the womb / uterus

c. Submucosal - towards the middle of the womb / uterus

It is estimated that between 20 to 50 percent of women of re-productive age have fibroids, although not all are diagnosed.

Diagnostic procedures for uterine fibroids may include: X-ray,Transvaginal ultrasound (also called ultrasonography)., Magneticresonance imaging (MRI) Hysterosalpingography, Hysteroscopy, En-dometrial biopsy, Blood test.Ultrasound imaging is a common modal-ity used for detecting fibroids. It is widely used in the field ofmedicine for imaging soft tissues in organs such as liver, kidney,spleen, uterus, heart and brain. The most noticeable advantagesof ultrasound scanning are safety, cost effectiveness, speed, easyhandling and portability.

The following are the steps to detect abnormalities using imageprocessing method

• Extract image data for processing

• Preprocessing to remove noise in image data

• Identify ROI

• Segment the desired area of image for analysis

• Extract the feature for classification

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2 Outline

Sectiion1.3 deals with data set used for detection of uterus fibroids.Section 1.4 describes preprocessing techniques of input image. Sec-tion 1.5 discusses various uterus image segmentation algorithms andcomparisons. Section 1.6 comprises fibroid detection and classifica-tion techniques. Section 1.7 concludes the survey.

3 Dataset

Ultrasound scanned uterus Image is used as Input. The uterus ul-trasound image scanning is performed either abdominally or transvagi-nally. Ultrasound examination can show the difference betweencysts and solid tumors such as fibroids. It cannot accurately di-agnose the number, size or position of the fibroids. Due to highresolutions and a large number of image slices Ultrasound Imagescannot be examined manually.

Figure1.Ultrasound scanned uterus image

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Fig 2 Process flow diagram

4 Preprocessing

Process of noise reduction is preprocessing. The quality of ultra-sound images is limited by granular speckle noise. The speckle ischaracterized by sudden changes in pixel intensities. It reducescontrast and obscures diagnostically important details. Boundaryedges of image are usually incomplete, being missing or weak atsome places. This makes it difficult to segment the ultrasound im-ages.

The Sequence [4] of preprocessing steps are as follows

• Read the image.

• Cropping unnecessary portions

• Resizing

• Morphological Operation, Filters, Thresholding and Wavelets

Ultrasound scanned images have some special features that mustbe preserved by the filtering, such as bright large scale interfaces

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between organs, structures with dimensions comparable to specklesize, and boundaries between two regions with slightly different graylevels. Therefore, linear nonadaptive smoothing techniques usedfor other image processing purposes may not be adequate for ultra-sound images. Many of such techniques introduce severe blurringand/or show unacceptable performance in elimination of speckle.

Lidiya Lilly Thampi et al uses SRAD filtering for speckle noisereduction. SRAD preserves edges and magnify edges by inhibit-ing diffusion across edges and allowing diffusion on either side ofthe edges. This can eliminate speckle without distorting useful im-age information and without destroying the important image edges.But this technique contains some speckle artifact and the imple-mentation is not so practical. According to Lee [46], image can beclassified into two categories. One is to average several looks ob-tained from the same scene. This category is equivalent to applyinga low pass (LP) filter. The other one is to smooth the speckle afterimages have been formed.

J. Saranya et al describes the enhanced lee filter and is used toclean the image. This method has the highest speckle suppressionrate and is able to deal with various types of noises but it smoothenthe edges and textures in the image.

Mustafa Karaman et al proposes filtering with appropriatelyshaped and sized local kernels. The filtering kernel, fits to thelocal homogeneous region containing the processed pixel and is ob-tained through a local statistics based region growing technique.P.S. Hiremath et al compare performance of the multiscale meth-ods, namely, wavelet transform, Laplacian pyramid transform andcontourlet transform for despeckling medical ultrasound images.Wavelet based despeckling is among the best methods for ultra-sound images. But it lacks shift invariance and poor directionalselectivity. Contourlet transform using hard thresholding is an ex-cellent tool for despeckling medical ultrasound images. By error dis-tribution the contourlet scheme provides a better overall improve-ment. This fixes subband mixing problem and improves directional-ity . The contourlet transform based despeckling method producesbetter quality ultrasound images for subsequent computer-assistedimage analysis by medical experts. This method does not produceoptimal segmentation results for all the sample images. Laplacianpyramid method gives multiscale decomposition of image but over-

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sampling is a disadvantage.Sean Finn et al have detailed a description and comparison of

speckle reduction of medical ultrasound, and in particular echocar-diography. The filtering techniques considered include anisotropicdiffusion, wavelet denoising, and local statistics. The anisotropicdiffusion (in particular OSRAD) and NMWD filters exhibit thestrongest speckle suppression. The wavelet-based methods are notcapable of a comparable level of speckle removal .This method alsoshows favorable edge preservation and contrast improvement, andmay be efficiently implemented. Author concludes that SRAD fil-ter is good for despeckling. Luc Vincent describes grey scale imagereconstruction method. Here binary and grey scale image recon-struction method sequential, parallel and hybrid type is described.Parallel reconstruction works by iterating elementary dilation fol-lowed by point wise minimum until stability. Implementation iseasy and efficient since image scanning is doing in arbitrary order.It requires the iteration of numerous complete image scanning. Itis therefore not suited to conventional computers, where its exe-cution time is often of several minutes. Sequential reconstructionalgorithm work with the following two principles:

1. The image pixels are scanned in a predefined order, generallyraster or anti-raster

2. The new value of the current pixel, determined from the val-ues of the pixels in its neighborhood, is written directly in thesame image.

This algorithm usually requires a few image scanning until sta-bility is reached, and is therefore much more efficient than the par-allel algorithm. But it does not deal well with rolled-up structures-connected components in the binary case and crest-lines in thegrayscale case. These two algorithms have complementary draw-backs and advantages. To overcome the drawbacks, it is proposeda method hybrid grey scale reconstruction algorithm and the ideais to start with the two first scanning of the sequential algorithm.During the second scanning (anti-raster), every pixel p such thatits current value could still be propagated during the next rasterscanning.

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Jiankang Wang et al propose systolic snake method to the nonhomogeneous scanned images which has intensity value changes,and incomplete boundary. Based on the observation that bound-aries in ultrasound images have the appearance of straight or gentlycurving line segments, author adopt Shaahsua and Ullmans saliencymap method to reduce speckle noise and enhance edges. Thismethod does not need a close initialization. Khalifa DJemal pro-poses method to reduce noise based on the calculation of an averageintensity in each pixel of the image by considering some neighbor.However this technique will attenuate the contour. This can bereduced by restoration method by minimization of total variation.

5 Segmentation and classification

Segmentation approaches can be divided into one of the follow-ing classes: thresholding, edge-detection, clustering, active contour,soft computing such as fuzzy logic and neural networks approaches,and region-growing. J. Alison Noble gives a detailed survey aboutultrasound image segmentation. Here author works on ultrasoundimage classied by clinical application and segmentation solutionsthat used other prior information (intensity, shape and temporalmodels). D. Jayadevappa et al proposed A Hybrid segmentationmodel based on watershed and gradient vector flow for the detec-tion of brain tumor. In this literature the researcher proposed thewatershed method with GVF snake to simplify the computationalcomplexity and improved the insensitivity to noise & capture range.Using this method,affected tumorous area is properly detected.

T.Akhila Thankam et al done clustering, particularly fuzzy C-means (FCM)-based clustering and its variants, have been widelyused in the task of image segmentation due to their simplicity andfast convergence. By carefully selecting input features such as pixelcolor, intensity, texture, or a weighted combination of these data,the FCM algorithm can segment images to several regions in ac-cordance with resulting clusters. This FCM algorithm is improvedby including the local spatial information of pixels in classical clus-tering procedures [53]. Yogita K et al introduces kernalization ofFCM to improve the performance. The kernel FCM (KFCM) al-gorithm is an extension of FCM, which maps the original inputs

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into a much higher dimensional Hilbert space by some transformfunction. Long Chen et al introduced a generalized multiple-kernelfuzzy C-means (MKFCM) methodology for segmentation of MRIbrain image. In MKFCM method different pixel information repre-sented by different kernels is combined in the kernel space to pro-duce a new kernel. These kernels are selected for different piecesof information or properties of image pixels. This algorithm pro-vides a signicant exibility in selecting and combining different kernelfunctions. Multiple-kernel methods provide us a great tool to fuseinformation from different sources.

Active contour models for segmentation can be classied as eitherparametric or geometric ones. Parametric active contour utilizesparametric equations to explicitly represent evolving curves, whilegeometric active contour implicitly represents evolving curves byusing the zero level set of a signed distance function usually calledas a level set function. The level set function is dened as the clos-est distance between pixels and a given closed curve in an imagedomain, and the distances of points inside the curve are assignedpositive and are negative outside.

Osher and Sethian [56] rst introduced a level set function incurve and surface evolution. Caselles et al. proved the connec-tion between energy-based and geometrical ows and presented anew geodesic active contour model. However, these methods non-periodically solve a PDE to keep the level set function to be asigned distance function. This process is called reinitialization andis expensive in computation. To overcome this problem, Li et al.Introduced a variational level set method to eliminate reinitializa-tion by incorporating a penalizing term into the energy functionalused in the geodesic active contour model. However, this methodis a bit sensitive to the initial position of the evolving curve be-cause it can just drive the curve evolving in the direction specied inadvance. Chan and Vese proposed a CV model based on regionalinformation. This method drives the evolving curve by minimiz-ing the tting error between the image and its piecewise constantrepresentation. Although it can detect more boundaries even in-cluding interior boundaries, the method cannot well deal with thesegmentation of nonhomogeneous objects. The CV model was alsoextended to detect more objects by using multiple level set func-tions.

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Belaid et al [28] presents a method in a variational level setframework for ultrasound images segmentation. The conventionalintensity gradient based methods have had limited success on ul-trasound images.Phase based method changes this condition. Thisidea is to use a novel speed function, which combines the local phaseand local orientation in order to detect boundaries in low contrastregions. Phase-based level set segmentation method can robustlyhandle noise, and captures well the low contrast boundaries. XinboGao et al [30] have proposed a image segmentation method thatapplies an edge-based level set method in a relay fashion. Thismethod segments an image in a series of nested sub regions thatare automatically created by shrinking the stabilized curves in theirprevious sub regions. The final result is obtained by combining allboundaries detected in these sub regions. This method performsbetter than other level set methods, and it can obtain similar or bet-ter results compared with other popular segmentation algorithm.Lidiya Lilly Thampi et al segmented endometrial cancer using levelset and Otsu thresholding algorithm. Otsu method segments thelesion region clearly than the level set method.

The active contour methods based on the theory of surface evo-lution and geometric flows, are classified as, edge-based methodsand region-based methods. The edge based methods utilize imagegradient to stop the contours on the boundaries of desired objects.To enlarge the capture range of the force, a balloon force term isoften incorporated into the evolution function, which controls thecontour to shrink or expand. However, it is difficult to choose aproper balloon force because too large or too small balloon forcewill result in undesirable effects. In case of region-based methodsutilize the image statistical information to construct constraints,and have more advantages over edge-based methods. Firstly, inthis method image gradient is not used so it can successfully seg-ment objects with weak boundaries or even without boundaries.Second, the initial contour can start anywhere in the image, andthe interior contours can be automatically detected.

One of the most frequently used region-based method is theChan-Vase (C-V) method [51], which has been successfully usedin binary phase segmentation with the assumption that each im-age region is statistically homogeneous. However, the C-V methoddoes not work well for the images with inhomogeneity. In [10]

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H Prasanna Kumar et al proposes improved active contour with-out edge method to detect polycystic ovary syndrome (PCOS). Im-proved Chan-Vase method, utilizes the local image information toconstruct a local image fitting energy and used to segment imageswith inhomogeneity. Further, the method is helpful in fast seg-mentation and detecting small follicles less than 2mm diameter. Inaddition, reinitialization is not needed in the proposed method.

In [18] author presents a procedure for automatic extractionand segmentation of a class-specic object (or region) by learningclass-specic boundaries. Here uses watershed transform that con-verts the image into an edge map that contains the lesion boundary.Feature-free boundary representation method that is based on vi-sual words and a visual dictionary. Viewing the watershed map asa Markov random field (MRF) in which each watershed superpixelcorresponds to a binary random variable indicating whether the su-perpixel is part of the lesion. The final segmentation is obtained byapplying a belief-propagation (BP) algorithm on the loopy MRFin-put. Automatic extraction interactive phase is very fast and userfriendly. Most of the patches in the watershed edge map are not onthe lesion boundary.

SetuGarg et al proposes Detection of Cervical Cancer by Us-ing Thresholding & Watershed Segmentation.MR Image is pre pro-cessed using edge detection tool of image processing. Then it issegmented using multiple techniques like thresholding and water-shed morphology. Malarkhodi.S et al,segmented the uterus imagebased on texture feature by reducing the noise. For effective imagesegmentation Expectation-Maximization (EM) algorithm based onGabor filter is used.

Chuen-Tsai Sun et al has provided a neuro-fuzzy classifier andits applications. The result of the adaptively adjusted classifierperforms well on an Iris classification problem. The results arediscussed from the viewpoint of feature selection. Anant Madab-hushi et al have automatically detected tumors and extracting le-sion boundaries in ultrasound images. Here, combining intensityand texture with empirical domain specific knowledge along withdirectional gradient and a deformable shape-based model. Proba-bilistic classification of image pixels based on intensity and textureis followed by region growing using the automatically determinedseed point to obtain an initial segmentation of the lesion. Zhang et

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al (2004) have investigated Genetic Algorithm based feature selec-tion method.

Jyothi R Tegnoor et al proposes contourlet transform basedmethod for preprocessing, active contours without edge based methodfor segmentation and SVM based method for classification. Num-ber of follicles, size, position and response to hormonal stimulation.Upon the detection of the follicles, the ovary is classified as normal,cystic and polycystic, on the basis of two parameters, namely, thenumber of follicles and the size of follicles in an ovary noise areeliminated by morphological erosion .

6 Detection of uterine fibroids

Shivakumar K et al proposed Segmentation and Analysis of Fi-broid from Ultrasound Images using GVF snake method. The tra-ditional Active Contour or Snake is automatically generated curvein an image to locate the object boundaries or it is a traditionalmethod of capturing the object, i.e. it starts from close to theboundary of object and covers the boundary concavities. Disad-vantage of the traditional Snake is, it cannot completely cover theboundary concavities and cannot capture longer range. So GVFsnake is proposed because it can capture longer range and accu-rately segment the object. N.Sriraam et al presents an automateddetection of uterine fibroid by using wavelet features and a neuralnetwork classifier. Based on user-defined ROI, athree level waveletpacket decomposition is applied. In order to distinguish the normaland fibroid uterus images, a feed forward backpropogation neuralnetwork (BPNN) classifier is used.

Yixuan Yuan et al propose a novel weighted locality-constrainedlinear coding (LLC) method followed by a weighted max-poolingmethod. Leonardo Rundo et al proposes a semi-automatic ap-proach, based on region-growing segmentation technique. Authorcombine splitandmerge and multiseed region growing algorithmsfor uterine fibroid segmentation in MRgFUS treatments. Here al-gorithm segments multiple fibroids with different pixel intensity,even in the same MR image.

Bo Ni et al proposes dynamic statistical shape model (SSM)-based segmentation method. For accurately learning prior shape in-

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formation of lesion boundary fluctuation in training set and Fokkerplank equation are incorporated into SSM(SF-SSM).This help toimprove segmentation accuracy incase of complex variable shape ofROI in images. This can improve efficiency and stability of seg-mentation. Alireza Fallahi et al proposes two step method. Thefirst step results in a uterine segmentation using FCM and somemorphological operations. In the second step by applying a newmethod based on FCM, PCM and information of voxels neigh-borhoods (Modified PFCM MPFCM) and knowledge based imageprocessing, final segmentation created. Long Chen et al general-ized multiple-kernel fuzzy C-means (FCM) (MKFCM) methodologyhave introduced as a framework for image segmentation problems.A linear combination of multiple kernels is proposed and the up-dating rules for the linear coefficients of the composite kernel arederived as well.

T.Ratha Jeyalakshmi et al discribes mathematical morphologyfor automated segmentation in which there is no need for the userto provide a seed point to segment the image. It completely avoidsover segmentation which is a major problem in morphological seg-mentation. The performance of this method is good and it takesless time to process.

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Table 1. Detailed survey on fibroid detection method andperformance

7 Conclusion

In this work, importance of uterus fibroid detection is discussed.Ultrasound images are affected by speckle noise and several specklenoise reduction techniques are discussed. Many ultrasound imagesegmentation methods to detect the abnormality in images alsodiscussed in this survey. This survey also supports a few fibroiddetection methods and only a few techniques are proclaimed infibroid detection. Some of the algorithms have high computational

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complexity, more execution time and less accuracy. Neither methodalone can handle all the complex fibroids and accurate segmentationor classification. Drawbacks of using the above techniques can beovercome by using machine learning technique.

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Detection of Uterus Fibroids in Ultrasound Images: a survey · 2018-03-15 · Detection of Uterus Fibroids in Ultrasound Images: a survey Dilna K T1 and D.Jude Hemanth2 1Department