Insights: Using the Likelihood Ratio in Bloodstain Pattern Analysis

INSIGHTS

Using the Likelihood Ratio in Bloodstain Pattern Analysis

OVERVIEW

Using likelihood ratios (LRs) when reporting forensic evidence in court has significant advantages, as it allows forensic practitioners to consider their findings from the perspective of both the defense and the prosecution. However, despite many organizations adapting or recommending this practice, most experts in the field of bloodstain pattern analysis (BPA) still use a more traditional, subjective approach, indicating whether their findings are “consistent with” stated allegations. Researchers funded by CSAFE explored the challenges that come with using LRs when reporting BPA evidence, and proposed possible solutions to meet these challenges, concluding that the LR framework is applicable to BPA, but that it is a complex task.

Lead Researchers

Daniel Attinger
Kris De Brabanter
Christophe Champod

Journal

Journal of Forensic Sciences

Publication Date

29 October 2021

Publication Number

IN 123 BPA

Goals

1

Determine why many BPA experts do not use LRs in their reporting

2

Present directions the community could take to facilitate the use of LRs

3

Provide an example of how LRs are applied in a relevant field

CHALLENGES
OF USING LIKELIHOOD RATIOS

Likelihood ratios (LRs) compare two competing hypotheses to see which better fits the evidence. While this practice has several advantages for use in court, as it provides a more objective and transparent view of an expert’s findings, there are challenges when it comes to applying LRs to bloodstain pattern analysis.

Graph displaying factors that can affect the complexity of BPA

Attinger et al. identified two key factors affecting a likelihood ratio’s complexity:

This is further complicated by the nature of bloodstain pattern analysis itself. BPA focuses on questions of activity (how far, how long ago, in what direction the blood traveled) or the type of activity (what caused the blood pattern), rather than questions of source as is normal for most forensic LR models. In addition, BPA as a field consists of a wide range of methods, and is a complex science that is still being built.

EXAMPLE OF LIKELIHOOD
RATIOS IN ACTION

A recent study demonstrated how LRs could be used in BPA by applying them to the related field of fluid dynamics. In their test, they compared the time between the drying of a blood pool in a laboratory setting and one observed in photographs.

Using this model, they were able to create a physical model factoring in time, the scale and shape of the blood pool, and the surface on which the pool formed. This model could then be applied into a likelihood ratio, comparing propositions from the prosecution and defense.

In this instance, the defense’s proposition would be 2330 times more likely than the prosecution’s.

Focus on the future

Attinger et al. propose three directions to facilitate the use of LRs in the field of BPA:

 

Promote education and research to better understand the physics of fluid dynamics and how they relate to BPA

Create public databases of BPA patterns, and promote a culture of data sharing and peer review

Develop BPA training material that discusses LRs and their foundations

Insights: Recognition of Overlapping Elliptical Objects in a Binary Image

INSIGHTS

Recognition of Overlapping Elliptical Objects in a Binary Image

OVERVIEW

A common objective in bloodstain pattern analysis is identifying the mechanism that produced the pattern, such as gunshots or blunt force impact. Existing image-based methods often ignore overlapping objects, which can limit the number of usable stains. Researchers funded by CSAFE established a novel technique for image analysis to provide more accurate data.

Lead Researchers

Tong Zou
Tianyu Pan
Michael Taylor
Hal Stern

Journal

Pattern Analysis and Applications

Publication Date

4 May 2021

Publication Number

IN 121 BPA

Goals

1

Develop a method to classify shapes in complex images.

2

Apply this method to data of different types including bloodstain patterns.

3

Compare the new method’s accuracy to existing methods.

Approach and Methodology

When analyzing bloodstain patterns, the individual stains may appear as clumps comprised of overlapping objects (e.g., droplets). Zou et al. developed a new computational method that identifies the individual objects making up each clump. The method proceeds as follows:

1

Generate a large number of elliptical shapes that match the overall contours of the clump.

2

Use an empirical measure of fit to reduce the set of candidate ellipses.

3

Identify concave points in the clump’s contour and set up an optimization to determine the best fitting ellipses.

Image Processing

Examples of ellipse fitting results for synthetic data. (a) Original binary image; (b) Ground truth; (c) DEFA model; (d) BB model; (e) DTECMA. The number of true ellipses increases from 2 (leftmost column) to 9 (rightmost column). Rows (c) and (d) are results from existing methods; row (e) gives results for Zou et al.’s DTECMA algorithm.

The researchers tested the method on a set of over 1,600 test images with overlapping shapes, emulating bloodstains (row a).

Study Results

  • Across four different metrics, the new approach outperformed existing approaches.
  • The current methods struggled to correctly recognize shapes as the number of ellipses per picture grew. Only the new method was able to maintain consistent accuracy.

Examples of ellipse fitting results for synthetic data. (a) Original binary image; (b) Ground truth; (c) DEFA model; (d) BB model; (e) DTECMA. The number of true ellipses increases from 2 (leftmost column) to 9 (rightmost column). Rows (c) and (d) are results from existing methods; row (e) gives results for Zou et al.’s DTECMA algorithm.

Focus on the future

 

The new approach to identifying elliptical-shaped objects in complex images shows marked improvement over current methods. This is demonstrated using simulated data and biological data for which the underlying truth is known.

While these results are promising, there is currently no way to quantify the performance of these models for bloodstain pattern analysis. The paper shows that the new method seems to do well based on visual inspection.

The next stage of the research is to use the identified ellipses as summaries of the images that can be used to develop statistical methods for analyzing bloodstain patterns.