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«Automatic Identification of Miscarriage Cases Supported by Decision Strength Using Ultrasound Images of the Gestational Sac Shan Khazendar1 Jessica ...»

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KHAZENDAR et al.: DECISION STRENGTH 1

Annals of the BMVA Vol. 2015, No. 5, pp 1−16 (2015)

Automatic Identification of

Miscarriage Cases Supported by

Decision Strength Using Ultrasound

Images of the Gestational Sac

Shan Khazendar1

Jessica Farren2, Hisham Al-Assam1, Hongbo Du1, Ahmed Sayasneh2, Tom Bourne2,

Sabah Jassim1

1

Department of Applied Computing, University of Buckingham, Buckingham, UK.

2

Department of Early Pregnancy, Imperial College, Queen Charlotte's and Chelsea Hospital, London, UK.

shan.khazendar@buckingham.ac.uk, jessicafarren@doctors.org.uk Abstract Ultrasound imaging is one of the most widely used multipurpose imaging modalities for monitoring and diagnosing early pregnancy events.

The first sign and measurable element of an early pregnancy is the appearance of the Gestational Sac (GS).

Currently, the size of the GS is manually estimated from ultrasound images.

The manual measurements tend to result in inter-  and intra-  observer variations, which may lead to difficulties in diagnosis.

This paper proposes a new method for automatic identification of miscarriage cases in the first trimester of pregnancy.

The proposed method automatically segments the GS and calculates the Mean Sac Diameter (MSD) and other geometric features of the segmented sac.

After classifying the image based on the extracted features into either a pregnancy of unknown viability (PUV) or a possible miscarriage case, we assign the decision with a strength level to reflect its reliability.

The paper argues that the level of decision strength gives more insight into decision making than other classical alternatives and makes the automated decision process closer to the diagnosis practice by experts.

1 Introduction Medical imaging techniques have been increasingly deployed in the past few decades to assist diagnoses of various types of diseases.

Among medical imaging modalities, ultrasound imaging is considered to be safe, non- invasive, portable, accurate, and cost effective.

These advantages have made ultrasound imaging the most common diagnosis tool deployed in hospitals around the world [Michailovich and Tannenbaum, (2006)].

Ultrasound imaging is also considered as an effective modality particularly for monitoring © 2007. The copyright of this document resides with its authors.

It m

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pregnancy [Joseph et al.

(2012)].

Monitoring within this period enables clinics to evaluate the development, growth, and wellbeing of the foetus [Kaur and Kaur, (2011)].

The first sign and measurable element of early pregnancy is the GS.

The American College of Radiology guideline defines miscarriage as being an empty GS with a MSD greater than or equal to 16 mm [Levis et al.

(1990)].

A recent study reviewed this cutoff [NICE clinical guideline, (2012)] and concluded that an empty GS with MSD greater than or equal to 25 mm should be introduced as a new guideline in order to minimize the risk of false positive diagnosis of miscarriage [NICE clinical guideline, (2012)].

This is irrespective of the assumed gestation of the pregnancy (calculated from the last menstrual period).

If the empty gestation sac does not meet these diagnostic criteria for a miscarriage, a repeat scan

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viability (PUV) – denoting the fact that there is neither evidence of viability, nor conclusive evidence of miscarriage.

Estimating the size of the GS is currently done manually.

The manual process involves multiple subjective decisions when the three diameter measurements on the GS, explained

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[NICE clinical guideline, (2012)].

The subjective decisions increase the interand intraobserver variations [Pexsters et al.

(2011)] which may lead to errors at diagnosis, and requires there to be margin of safety in the diagnostic criteria chosen.

An automated way of estimating the size of the GS from a given ultrasound image is therefore desirable.

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ultrasound image first.

Unlike other types of medical images, ultrasound images are corrupted by speckle noise that tends to reduce image quality and contrast, and consequently increases the level of difficulty in segmenting GS.

A considerable amount of research into ultrasound image denosing has been undertaken [Jawad, (2007), Hiremath et al.

(2009), Hiremath and Tegnoor, (2010)], but the research in automatic GS segmentation is limited.

In [Chakkarwar et al.

(2010)], a method that uses a combination of contrast enhancement, low pass filtering and Wiener filtering to despeckle the image, followed by thresholding, was reported with an average accuracy of 83.3% over a small database of 12 images.

In [Zhang et al.

(2011)] an algorithm for detecting GS from a video

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followed by exploiting the local context and eliminating false positive detections.

The algorithm was tested on 31 videos and achieved a GS detection rate of 87.5%.

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estimate the size of the GS in terms of the MSD.

We used the k Nearest Neighbour (kNN) [Du, (2010)] classifier to identify early miscarriage cases based on the automatically measured MSD.





We also compared the classification performance of using MSD with other geometric features from GS images such as volume, perimeter, area, circularity, compactness, solidity and eccentricity on a limited dataset of 68 images.

This paper extends our work presented in [Khazendar et al.

(2014)] in two ways.

First, we expand the existing dataset with more recently acquired images totalling 184 images to evaluate our

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method and consolidate our findings.

Second, we introduce the concept of decision strength levels into the classification stage.

We argue that the level of decision strength provides more insight than other classical classification methods and makes the proposed decision making closer to the practical diagnosis of miscarriage cases by experts.

Our experimental results using the 184 images confirm that the proposed solution achieves a high level of accuracy using the automatically estimated MSD as well as the perimeter and volume of the sac.

2 Miscarriage Diagnosis Based on Manual Measurements An ultrasound machine is a realtime imaging device which can scan the region of interest using different probe angles.

Gynaecologists usually scan the image in the sagittal plane, i.e.

the vertical longitudinal plane of the uterus, locate the GS, and select two points on the boundaries of the GS using joy sticks to measure its diameters (major and minor).

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transverse plane, i.e.

the horizontal plane that is perpendicular to the coronal and sagittal planes, and then takes the third diameter measurement.

The MSD is defined as the average of these three measurements.

Diagnosis is made according to the refined NICE guideline

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obvious contents (Yolk Sac (YS) or embryo) inside, miscarriage is pronounced.

Fig 1 presents an example image of the GS taken in respective sagittal and transverse planes.

The red rectangle represents the main fan area.

The GS is the dark region in the center.

The area outside the red rectangle is called margin area.

It shows information about the patient (blocked for anonymity), the date and time that the image was taken and the ultrasound machine setting.

The figure shows the three manual measurements of the GS size marked by yellow dotted lines.

The measurement results in millimeters are present in the margin area.

There are other signs of likely miscarriage.

For example, the border of the GS appears irregular in its shape.

Although a GS growth abnormality is within our scope of investigation, this paper is only concerned with the miscarriage diagnosis based on the GS size measurements.

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Figure 1:

Ultrasound image of GS in Sagittal and Transverse planes 3 The Proposed Method for Automatic Miscarriage Diagnosis 3.1 Materials The ultrasound images used in our study were obtained in two batches.

The first dataset contains 94 ultrasound

images:

79 images are PUV cases and 15 images of miscarriage cases.

The second independently sampled dataset contains 90 images among which 78

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points of time in the first trimester of pregnancy, collected and labelled by the author (JF) in the Early Pregnancy Units, Imperial College Healthcare Trust, London, UK.

Each image consists of two views of a GS from two perpendicular sections/planes as explained in section 2.

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Fig 2 shows the block diagram of the underlying process of the proposed solution for automatic identification of miscarriage cases.

The process consists of a sequence of stages starting from cropping a region of interest, followed by enhancing the image, segmenting GS from the enhanced image, extracting diameter measurements of best fit ellipse shape, and finally classifying the GS as miscarriage or PUV based on the [NICE clinical guideline, (2012)], with a classification strength indicator.

Each stage of the process will be explained in details in the following subsections.

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Figure 2:

Block Diagram of the Major Steps of the Proposed Method 3.2.1 Image Cropping Each original input image contains twoplane views of the GS with the margin areas.

Before the GS segmentation starts, the margin areas of both views need to be removed by using the imcrop function in Matlab with a fixed position vector parameter (30, 150, 900, 500) where the first two components represent the coordinates of the topleft corner point and the latter two components represent the width and height of the crop region.

The cropped image is as shown in Fig 3(a).

Then we separate the two views from the middle of the image.

The resulting two images are shown in Fig 3(b).

3.2.2 Image Enhancement Ultrasound images of the GS are typically dark, causing difficulties in segmenting the GS.

We used the following heuristics filter to enhance the

image:

where I(i,j) is the intensity value of the pixel at (i, j) position, and µ is the mean of all pixel intensity values.

Unlike histogram equalisation, this simple pixel value transformation gives more weight to dark pixels where the detailed information lays by stretching them over the whole greyscale range.

The main aim of this enhancement is to highlight the GS for ease of segmentation.

The highlighted area of the GS is as shown in Fig 3(c).

3.2.3 GS Segmentation The GS segmentation stage involves a series of operational steps due to the noisy surroundings near the sac.

These steps are described as

follows:

Step 1:

Thresholding.

The Otsu thresholding method [Otsu, (1979)] is first applied to the

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without losing its original shape, fills small holes/gaps in the GS region, and helps connecting the nonsac or false regions to image borders for later removal.

Step 3:

False regions removal.

The imclearborder function in Matlab is then applied to clear all false regions that are connected to the image border, resulting in a clean image as shown in Fig 3(f).

Step 4:

Further noise removal.

Any small objects remaining in the image are considered as noises and should be removed.

This is done by labelling each object using the Matlab function bwlabel, calculating the area of each object, and then deleting all small objects.

The only remaining object is the GS as shown in Fig 3(g).

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Figure 3:

Steps of GS segmentation

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3.2.4 Feature Extraction As explained earlier, each GS is viewed in two perpendicular planes.

The GS is usually

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matching the normalized second central moments based on [Haralick and Shapiro, (1992)].

This function returns four

parameters:

Major and Minor axes, Centroid and Orientation which is the angle between major and minor axes, as shown in Fig 4.

Assuming the GS has an ellipsoidal shape in 3D, the three principal axes of the ellipsoid can be estimated by the major axis (A), minor axis (B) of the ellipse from the sagittal plane and the major axis (C) from the transverse plane.

Figure 4:

Best fitting ellipse for feature extraction After that, we extract the following geometric features from each

GS:

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For each image, the extracted features can be treated as separate features or as components of a feature vector for diagnosis purposes.

To highlight the usefulness of these features at

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images of PUV cases, and plot the automated measurements upon these features in Fig 5 The scatter plots show a clear separation of miscarriage and PUV cases.

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Figure 5:

Scatter plots of automated measurements upon the three features for sample images 3.2.5 Classification In principle, any appropriate classifier can be trained and deployed in this step.

In this particular study, we used a simple kNN classifier to evaluate the effectiveness of our segmentation and feature extraction methods.

The kNN classifier determines the class of a testing image by calculating the distances between the testing image’s feature or feature vector and that of each exemplar image in the training set, locating the k nearest exemplars and using majority voting to decide the class of the testing image.



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