Particle size distribution analysis

Abstract: A method for measuring and analyzing particle size distribution of extremely fine particles, such as blood corpuscles and cells, comprises collecting analyzing particle size distribution from the instru­ ment, setting an estimated distribution theoretically, comparing between the two distributions, determining the difference therebetween, and making the difference a characteristic parameter for the analyzing particle size distribution.

Description: The present invention relates to a method for measuring and analyzing particle size distributions, wherein the particles are blood corpuscles, various cells, latex particles or any other fine particles. The measurement and analysis of blood corpuscles are known in the art, but it is uncommon that they are applied to the clinical diagnosis. This is because of the difficulty and inaccuracy involved in conducting them. However the recent development of a fluid control system, such as sheath flow control system, has enhanced the accuracy of an automatic measuring method of particle size distributions, and has facilitated the application of the method for clinical purposes. At the same time this has aroused people's attention to the importance of analysis of particle size distribution. One example of the conventional analyses is disclosed in Japanese Patent Unexamined Publication No. 47(1972)-13299. This prior art method is to analyze the particle size distribution of red blood cells, and is characterized by the representation of the distribution area in terms of quartile variable coefficients. With respect to the particle size distribution of blood platelets it is presumed that the blood platelets are in a log-normal distribution, and then those which are not in agreement with the presumption are considered as abnormal, which is commonly called a 'Curve Fit' method. The feature of the first-mentioned prior art method for red blood cells resides in the indication of the width of the distribution on presumption that the particle size distribution of red blood cells is constantly uniform. In addition, the blood corpuscles are readily affected by dirt and dead cells. For these reasons the inaccurate measurement may result. Furthermore, the blood corpuscle measurements unavoidably contain noise components, so that the thresholds are used to count the number of corpuscles. Nevertheless, when a noise component and a corpuscle measurement overlap or two or more corpuscle measurements overlap each other, the resulting measurement may be inaccurate. The 'Curve Fit' method determines the abnormality of blood platelets on presumption that they are in the log-normal distribution but does not represent the degree of abnormality numerically, that is, in an objective manner. In order to solve the problems pointed out above The International Committee for Standardization in Haematology (ICSH) has issued an official recommendation in 1982, in which the theoretical distribution should be applied to particle size distributions. The Committee also gave a general statement in support of its recommendation. In US-4,128,884, a blood sample contains two types of cell. Thus, the measured particle size distribution has two peaks. The two peak frequencies are measured and are used to calculate a numerical value that represents the proportion of the first type of cell. US-4,488,248 discloses a method for measuring and analyzing particle size distribution, comprising the steps of: receiving the measured particle size distribution of a given sample from a particle size measuring instrument; and comparing the measured particle size distibution with a different particle size distribution. The reader is referred to Figures 4 to 8 of US-4,488,248 and the corresponding part of the description. The measured particle size distribution (e.g. 3001, 3002 or 3003 of Figure 4) is compared with a measured reference distribution (3004 of Figure 5) of a reference specimen. The difference is displayed on a graph (see Figure 6, 7 or 8) and the shape of the difference curve is used to make a diagnosis. According to the present invention, there is provided a method for measuring and analyzing particle size distribution, the method comprising the steps of: (i) receiving the measured particle size distribution of a given sample from a particle size measuring instrument; and (iv) comparing the measured particle size distribution with a different particle size distribution; characterized in that the different particle size distribution is a theoretical particle size distribution which is a normal or a log-normal distribution and the method comprises the further steps of: (ii) calculating a standard deviation and a mean value for the theoretical particle size distribution by analyzing the measured particle size distribution; (iii) producing the theoretical particle size distribution by using the calculated standard deviation and mean value; and (v) producing a parameter characteristic of the result of the comparison of step (iv), for use in classifying the measured particle size distribution. For a sample, such as a blood sample, that is known to have a normal or log-normal particle size distribution, depending on the medical condition of the blood, the present invention facilitates clinical diagnosis. The mean and standard deviation of the theoretical distribution are determined by analyzing the measured distribution. Thus, the theoretical distribution is customized to fit the measured distribution as closely as possible. The measured and theoretical distributions are compared and a parameter characteristic of the comparison result is produced. The comparison may involve subtraction in order to produce a characteristic parameter which is a matching distance (D), or the comparison may involve division in order to produce a characteristic parameter which is a matching ratio (P). Preferably, steps (ii) to (v) are performed a second time, with the theoretical particle size distribution being the one of the normal and log-normal distributions that was not used the first time, thereby to produce a different characteristic parameter. Preferably, the method further comprises the step of: (vi) classifying the measured particle size distribution on the basis of the characteristic parameter(s) and the type(s) of the theoretical distribution(s). Preferably, step (ii) comprises: (a) determining the logarithm of each frequency value of the measured distribution and determining the difference between each pair of adjacent logarithmic values to produce a series of differences approximating a derivative of the measured distribution; (b) finding a substantially linear portion of the series of differences; and (c) calculating the mean value and the standard deviation from the substantially linear portion. The invention will now be described by way of example only, reference being made to the accompanying drawings in which: Figure 1 is a flow chart showing a system embodying the present invention; Figure 2 is a graph showing an example analyzing normal red blood cells; Figure 3 is a graph showing an example analyzing red blood cells whose particle size distributions overlap each other; Figure 4 is a graph showing the distribution of matching distance (D) obtained when the estimated particle size distribution is a log-normal distribution; Figure 5 is a flow chart showing a modified version of the system embodying the present invention; Figures 6 to 9 are respectively graphs showing examples of the analyses; Figure 10 is a flow chart showing a further modified version of the system embodying the present invention; Figure 11 is a graph made by plotting the values of g′(x); Figure 12 is a graph showing an example in which the g′(x) has a plurality of linear portions; Figure 13 is a graph showing an example in which a blood platelet portion overlaps the red blood cell portion; Figure 14 is a graph showing an example in which there is little blood platelet; and Figure 15 is a graph showing an example of red blood cells having several theoretical distributions. To carry out the method described below, an instrument is used to measure particle size distributions. The instrument referred to herein is a device which allows a suspension containing fine particles to pass through the minute pores produced therein, detects the particles by electric or optical differences between the liquid and the particles, generates pulses in proportion to the amplitude (volume) of the particles, and converts the signals into a particle size distribution. Example 1 This is an example in which the method of the present invention was applied to the analysis of a particle size distribution of blood corpuscles, employing a log-normal distribution, which is expressed by: wherein µ is a mean value, and σ is a standard deviation. The natural logarithm of the f(x) is then expressed by: Substituting X and g(x) for l nx and l n f(x), respectively, we get: In this way g(x) is expressed by a quadratic expression with respect to X. Herein, g(x) is differentiated as follows: Thus g′(x) is expressed by a linear expression which intersects the X axis at µ - σ² and which is inclined thereto at From this the value of g′(x) is obtained, on the basis of which the mean value µ and the standard deviation σ are calculated. In this way the log-normal distribution curve is estimated. As an example of a parameter representing the difference between the estimated log-normal distribution and the particle size distribution of the original blood corpuscles the matching distance (D) is introduced, which is defined as follows: D = ∫ ( |f(x) - H(x)| ) dx wherein f(x) is the given distribution and h(x) is the estimated one. Referring to Figure 1 the blood corpuscle pulses proportional to the volume are converted into a volume distribution by a comparator, the result of which is stored in a memory. The stored data is smoothed, which means that the noises contained in the particle size distribution are removed so as to facilitate the analysis of particle size. Then the natural logarithm is obtained for the particle size distribution, and the balance between the adjacent values is calculated to determine the value of g′(x). Then a window (a range in which a treatment is applied to the X axis) is set so as to find a main portion of the given particle size distribution, and in this window a point where the g′(x) becomes linear or approximately linear is determined. The value of the linear portion of g′(x) is expressed by a regression line equation, and from the inclination and intersection of the equation the mean value of the estimated particle size distribution and the standard deviation are calculated. These values obtained are introduced in the log-normal distribution, and the maximum value thereof is normalized by the original particle size distribution; that is, the maximum values of the original particle size distribution and estimated distribution are equalized, thereby facilitating the comparison therebetween. Then the difference in area between the two distributions is calculated to determine the matching distance (D). The original particle size distribution is balanced by the estimated distribution to form a sub-distribution. If this sub-distribution has a sufficiently large area to allow another estimated distribution to be determined, the sub-distribution can be employed as the fresh original particle size distribution. This procedure is repeated. When the area of the sub-distribution is small the operation is finished. In one experiment 2185 specimens of red blood cells were tested to find the matching distance (D), and it was found that they were distributed in a rather wide range of 0 to 400, showing a log-normal distribution. Among the 2185 specimens 6.9% thereof had matching distance (D) of 100 or more, 82.1% of which were diagnosed as a malignant new organism and ulceration. The test revealed that 6.3% of the whole specimens suffered from these ailments. This proves that the method of the present invention can be used as a parameter for diagnosing patients' ailments through the diagnosis of blood. Referring to Figure 2 the graph shows a case wherein: Red Blood Cell (RBC) = 445 x 10⁴/mm³ Matching distance (D) = 6 In this graph the particle size distributions obtained and estimated almost overlap, thereby resulting in a single curve. Referring to Figure 3 the graph shows an example of analysis of red blood cells whose particle size distributions overlap, wherein: Red Blood Cells (RBC) = 317 x 10⁴/mm³ Matching Distance (D) = 66 The dotted lines show the estimated particle size distribution. Referring to Figure 4 the graph shows a relationship between the matching distance (D) and the number of specimens with respect to red blood cells, wherein the particle size distribution is a log-normal distribution. The same applies when the estimated particle size distribution is other than a log-normal distribution. The difference between the original particle size distribution and the estimated one can be represented by several parameters such as the one obtained from the following equation, wherein P is a matching ratio: Example 2 In Example (2) the method of the present invention was applied to the analysis of particle size distributions of red blood cells, employing a normal distribution and a log-normal distribution. The normal distribution is given by the following equations: wherein µ is a mean value, and σ a standard deviation. g(x) is substituted by l n f(x). Then, the above equation will be changed as follows: g(x) is differentiated: Thus, g'(x) is expressed by a linear expression which intersects the X axis at µ², and which is inclined thereto at From this the mean value µ and standard deviation σ are calculated. In this way the normal distribution curve is estimated. As an example of a parameter representing the difference between the estimated normal distribution and the original particle size distribution of blood corpuscles a matching distance (D) is introduced in the last-mentioned equation, wherein the matching distance (D) is expressed by: D = ∫ ( |f(x) - h(x)| ) dX wherein f(x) is a given distribution and h(x) is an estimated distribution. Referring Figure 5 blood corpuscles pulses proportional to the volumes are converted into volume distributions by using a comparator, and stored in a memory. Then the data stored therein is smoothed so as to facilitate the analysis of particle size distribution. The smoothing is intended to remove noise components from the particle size distribution. Then the natural logarithm of each distribution is obtained so as to calculate the difference between the adjacent values. In this way g'(x) is calculated. A suitable window is set to find a main portion of the given distribution, and in this window a point where g'(x) becomes linear or almost linear is determined. This point is the part of the particle size distribution which is identical with the normal distribution. A regression line equation of this linear portion of g'(x) is formulated, and from the inclination and intersection of the equation the mean value and standard deviation of the estimated distribution are calculated. Then the mean value and standard deviation are introduced in the normal distribution, and the maximum value obtained is normalized by the original particle size distribution; that is, the maximum values of the original particle size distribution and estimated distribution are equalized, thereby facilitating the comparison therebetween. Then the difference in area between the two distributions is calculated to obtain a matching distance (D). Figures 6, 7 and 8 show various types of graphs obtained when normal distributions are theoretically applied to analyze red blood cells; hereinafter these types will be referred to as Type I, Type II and Type III, respectively. The graph shown in Figure 6 almost overlaps the normal distribution. The dotted lines show the estimated particle size distribution which overlaps the original distribution to an undiscernible extent. The graph shown in Figure 7 shows a relatively large matching distance (D) and also shows that the distribution (indicated by the dotted lines) estimated from the normal distribution does not overlap the original distribution. The graph shown in Figure 9 has been obtained to estimate a particle size distribution by the use of a log-normal distribution as a theoretical distribution. This graph shows that the estimated distribution (indicated by the dotted lines) and the log-normal distribution almost exactly overlap each other. It will be appreciated that the matching distance (D) is about 1/6 as short as in the case of the normal distribution. Figure 8 shows an example where there is a large space remaining after the estimation was made by a single normal distribution, thereby allowing of a further estimation by the use of normal distribution. In other words, two normal distributions are allowed to estimate the distributions. This type of distribution is normally formed by the addition of the two normal distributions. 2500 specimens of blood were classified according to this type. As a result 51.3% belonged to Type I, 45.2% to Type II and 3.5% to Type III. Most of the blood belonging to Type III were found in the patients who suffered from the iron deficiency anemia. 325 specimens of blood were examined as to whether they had matching distance (D) of 60 or more under the normal distribution estimation and less than 60 under log-normal distribution estimation; as a result 39 specimens were selected, 35 specimens of which were collected from patients suffering from malignant new organism. In this way the particle size distribution is classified by a theoretical distribution or else by a matching distance (D), which means that they can be useful as new parameters for diagnosing blood. As mentioned above for parameters representing the difference between the estimated particle size distribution and the original one, the matching ratio (P) is expressed by the following equation: As seen from the equation various parameters can be used. EXAMPLE 3 There is a log-normal distribution which satisfies the following equation: wherein µ is a mean value, and σ is a standard deviation. Thus, the natural logarithm of f(x) is expressed by: Herein, X and g(x) are substituted by l nX and l nf(x), respectively. Then As evident from this, g(x) is expressed by a quadratic expression with respect to X. g(x) is differentiated as follows: g′(x) is expressed by a linear expression which intersects the X-axis at µ - σ² and is inclined thereto at With the value of g′(x) the mean value µ and the standard deviation σ are calculated, thereby estimating a log-normal distribution. Referring to Figure 10 blood corpuscle pulses proportional to the volumes are converted into volume distribution by a comparator, and stored in a memory. The particle size distribution data stored therein are smoothed so as to facilitate the analysis of particle size distribution, which means that the noise components in the distribution are removed. Then the natural logarithm of the frequencies of particle size distribution, and the difference between the adjacent distributions is calculated. The calculated differences are divided by a balance between the logarithms of the corresponding ranks. The quotients are plotted against the logarithm of each rank, which is shown in Figure 11. In this way g'(x) is calculated. Figure 12 shows an example in which g'(x) has several linear portions. In this case there are two theoretical distributions available. To determine the linear portions of g'(x) a window is provided and shifted along the X-axis. In this way linear or approximately linear portions are extrapolated, and from the intersections of the X- and Y-axis the mean value and standard deviation are calculated. Then these values are introduced in the log-normal distribution and the maximum value is normalized by the original particle size distribution; that is, the maximum value is equalized to that of the original particle size distribution, thereby facilitating the comparison between the original particle size distribution and the estimated one. Then the frequency of the estimated particle size distribution is determined. The plurality of linear portions of g'(x) leads to a plurality of theoretical distributions. Therefore, on the basis of each linear portion an estimated distribution and the frequency are obtained. The obtained data is output to a CRT, printer or host computer. The following cases are where the method is applied to blood corpuscles: (1) A case where a red blood cell portion overlaps a blood platelet portion: The graph of Figure 13 shows an example where a red blood cell portion overlaps a blood platelet (PLT) portion. The left-hand section shows the PLT portion, and the theoretical distribution is indicated by dotted lines. The right-hand section is the red blood cell portion, whose graph looks like a straight line because of the curve being cut away at a certain value. In view of the overlapping of the red blood cell portion and PLT portion the measured value obtained from the theoretical distribution was 163,000 PLTs, whereas that obtained by an ordinary threshold was 217,000 PTLs. (2) A case where a small quantity of PLT is present: When the blood has a small number of PLTs, such as some thousand to some ten-thousand of them, the impure factors such as noise adversely act, and lead to the inaccurate measurement, for which Figure 14 shows an example. The left-hand section is a noise portion; the middle section is the PLT portion, and the right-hand section is a red blood cell portion. The theoretical distribution is shown by the dotted lines. The measured value from the theoretical distribution was 44,000 whereas that from an ordinary threshold was 86,000. (3) A case where a red blood cell has two or more theoretical distributions: Referring to Figure 15 the theoretical distributions are shown by the dotted lines. The blood in this example contained different sizes of blood corpuscles because of transfusion, having 5,090,000 red blood cells (RBC), wherein the larger section had 3,660,000 RBCs and the smaller section had 1,420,000 RBCs. As is evident from the foregoing description of embodiments of the present invention, it is possible to remove irrelevant particles from the specimen particles, thereby securing a reliable measurement. When several distributions overlap, the described embodiments are particularly advantageous in measuring the respective distributions. As referred to above the theoretical distribution can be a normal or log-normal distribution. The method of the present invention can be equally applied to the analysis of latex particles, many kinds of cells or any other extremely fine particles. The main advantages of the described embodiments are as follows: (1) The adoption of a new parameter facilitates the recognition of characteristics of particle size distributions, and the collection of clinical data. (2) The abnormality can be numerically represented, thereby securing the objectiveness of the data. (3) The analysis of particle size distribution can be effectively conducted when several distributions overlap each other. (4) The number of blood corpuscles can be counted accurately. (5) The method of the present invention can be applied not only to blood corpuscles but also to latex particles, cells or any other extremely fine particles. (6) The counting of the particles can be conducted in a state free from noise or any other impure factors, thereby enhancing the accuracy and reliability of the data. (7) The method described above can be carried out on a relatively small-size apparatus, such as a blood corpuscle counter, thereby making the method applicable at a limited space in hospitals and laboratories. (8) In carrying out the methods, the particle size distribution to be diagnosed and the theoretical distribution to be used are simultaneously indicated, whereby the operator has an accurate information about them beforehand.

Claims: A method for measuring and analyzing particle size distribution, the method comprising the steps of: (i) receiving the measured particle size distribution of a given sample from a particle size measuring instrument; and (iv) comparing the measured particle size distribution with a different particle size distribution; characterized in that the different particle size distribution is a theoretical particle size distribution which is a normal or a log-normal distribution and the method comprises the further steps of: (ii) calculating a standard deviation and a mean value for the theoretical particle size distribution by analyzing the measured particle size distribution; (iii) producing the theoretical particle size distribution by using the calculated standard deviation and mean value; and (v) producing a parameter characteristic of the result of the comparison of step (iv), for use in classifying the measured particle size distribution. A method according to claim 1, wherein steps (ii) to (v) are performed a second time, with the theoretical particle size distribution being the one of the normal and log-normal distributions that was not used the first time, thereby to produce a different characteristic parameter. A method according to claim 1 or 2, further comprising the step of: (vi) classifying the measured particle size distribution on the basis of the characteristic parameter(s) and the type(s) of the theoretical distribution(s). A method according to any one of claims 1 to 3, wherein step (ii) comprises: (a) determining the logarithm of each frequency value of the measured distribution and determining the difference between each pair of adjacent logarithmic values to produce a series of differences approximating a derivative of the measured distribution; (b) finding a substantially linear portion of the series of differences; and (c) calculating the mean value and the standard deviation from the substantially linear portion. A method according to any one of claims 1 to 4, wherein step (iv) comprises determining the difference between the measured and the different particle size distributions, and the difference is used in step (v) to produce the characteristic parameter.

Inventors: KANAMORI SHIGEO, KANAMORI, SHIGEO
Applicants: TOA MEDICAL ELECTRONICS, TOA MEDICAL ELECTRONICS CO., LTD.
Application year: 1987
Main classification: G01N15
IPC codes: G01N15